IEEE Photonics Society

Boston Photonics Society Chapter

Boston Chapter of the IEEE Photonics Society

Machine Learning and Optical Systems
-- The workshop is being postponed to October due to COVID-19 --
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Wednesday, April 8, 15, 22, 29 and May 6, 2020, 7:00-9:30 PM
Located at MIT Lincoln Laboratory – 3 Forbes Road, Lexington, MA, 02420, USA

Wednesday
April 15, 2020
7 PM
 

-- The workshop is being postponed due to COVID-19 --: Advancing Optical Communication and Measurement Systems Using Machine Learning"/>Add to Calendar Add to Calendar

Advancing Optical Communication and Measurement Systems Using Machine Learning

Prof. Darko Zibar, Technical University of Denmark, Kongens Lyngby, Denmark

 

Prof. Darko Zibar, Technical University of Denmark, Kongens Lyngby, Denmark

Abstract:  According to the recent data traffic predictions, current optical communication systems, operating in the C--band only, will not be able to satisfy future data rate demands. A viable and long--term solution would be to employ systems operating in multiple bands (O+E+S+C+L) and make usage of the spatial division multiplexing (SDM) (multi--core and multi--mode). Designing optimal signaling, amplification and detection schemes, for such systems, will be challenging due to the high system complexity. Finally, performing system optimization in terms of channel power and bandwidth allocation, as well as modulation format selection, will become difficult using standard tools that rely on analytical or semi--analytical models. What will complicate the matter even further is the focus on providing a secure way of transmitting information using quantum communication.  This will require a coexistence and management of classical and quantum channels in the same optical network. As quantum signals have in general significantly, lower powers compared to the signals in classical communication, the reception of quantum signals is more challenging, making a strong case for having intelligent optical receivers that can receive and even distinguish between classical and quantum signals.

The field of machine learning (ML) can provide useful tools to address the aforementioned challenges. This is because ML techniques excel at: 1) learning highly--complex input--output mappings which allows for system optimization, 2) learning signaling and detection schemes for complex channels or for channels where analytical models are not available and 3) performing ultra--sensitive signal detection. In this talk, it will be shown how machine learning can enable design of ultrawide-band optical amplifiers, perform constellation shaping over the nonlinear fibre optic channel and enable ultra-sensitive measurements of optical phase that approach the quantum limit.

 

Biography:  Darko Zibar is Associate Professor at the Department of Photonics Engineering, Technical University of Denmark and the group leader of Machine Learning in Photonics Systems (M-LiPS) group. He received 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 has been on several occasions (2006, 2008 and 2019) visiting researcher with the Optoelectronic Research Group led by Prof. John E. Bowers at the University of California, Santa Barbara, (UCSB). At UCSB, he has been working on topics ranging from analog and digital demodulation techniques for microwave photonics links and machine learning enabled ultra-sensitive laser phase noise measurements techniques. In 2009, he was a visiting researcher with Nokia-Siemens Networks, working on clock recovery techniques for 112 Gb/s polarization multiplexed optical communication systems. In 2018, he was visiting Professor with Optical Communication (Prof. Andrea Carena, OptCom) group, Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino working on the topic of machine learning based Raman amplifier design. His resrearch efforts are currently focused on the application of machine learning technqiues to advance classical and quantum optical communication and measurement systems. Some of his major scientific contributions include: record capacity hybrid optical-wireless link (2011), record sensitive optical phase noise measurement technique that approaches the quantum limit (2019) and design of ultrawide band arbitrary gain Raman amplifier (2019). He is a recipient of Best Student paper award at Microwave Photonics Conference (2006), Villum Young Investigator Programme (2012), Young Researcher Award by University of Erlangen-Nurnberg (2016) and European Research Council (ERC) Consolidator Grant (2017). Finally, he was a part of the team that won the HORIZON 2020 prize for breaking the optical transmission barriers (2016).

 

Advance registration and fee required (Open to all IEEE members as well as non-members)

$50/$60 (IEEE Member/Non-Member) early registration fee for ten 1-hour talks over five nights; cost includes coffee and cookies each night, as well as downloadable copies of speakers slides. Early registration deadline March 18th, 2020. Post deadline fee $60/$70 (IEEE Member/Non-Member).


For more information on the technical content of the workshop, contact either:
1) Keisuke Kojima, (kojima@merl.com), Chair
2) Ajay Garg, (ajay.sinclair.garg@ieee.org), Co-Chair
3) Dean Tsang, (tsang@ieee.org), Co-Chair
4) Bill Nelson, (w.nelson@ieee.org), Co-Chair