Title: On Probabilistic Shaping and Learned Decoders with Application to Fiber-Optic Communications
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The PhD defence can be accessed through Zoom, and the it will open shortly before 10:00. We would kindly ask you to keep the video off and mute the microphone during the seminar. At the end of the session there will be an opportunity to ask questions through Zoom. In case there will be any updates about the event, these will be posted on this website.
Andreas Buchberger is a PhD student in the research group of Communikationssystems
Faculty Opponent is Professor Guido Montorsi, Politecnico di Torino, Italy
Examiner is Professor Alexandre Graell i Amat in the research group of Communikationssystems
Optical fibers form the backbone of today’s communication networks. Every phone call, text message, email, or website will, at some point, be transmitted through an optical fiber where light carries data from the transmitter to the receiver. Data is usually represented in binary form as a sequence of zeros and ones, both being equally likely (i.e., if we have a long sequence of bits, we know that about half of them are zero and half of them are one). However, it is known that transmitting such a stream of equiprobable bits is not optimal and does not result in the highest possible transmission rate. In the first part of this thesis, we design a system that transforms the sequence of equiprobable bits into a sequence where zeros are more likely than ones. This way, we can increase the transmission rate.
Every communication system is subject to noise caused by different physical phenomena such as random thermal movements of electrons. When transmitting data, it hence may happen that we receive a zero when a one was sent and vice versa. By using error correcting codes, some of these transmission errors can be corrected at the receiver side. In the second part of this thesis, we use machine learning to implement these error correcting codes at the receiver and improve the reliability of the transmission.
27 January, 2021, 10:00
27 January, 2021, 13:00