ACCESS Distinguished Lecture Series
Title: The Deep Learning in Physical Layer Communications.
If you cannot make it to KTH, join the live stream of the lecture at www.youtube.com/c/KTHMediaProduction
Also notice that you can view previous ACCESS DLS seminars at https://www.access.kth.se/en/aboutaccess/newsandevents/seminars/dls
Professor Carlo Fischione, EECS / NSE
Time: Thu 2019-04-11 15.00
Location: Lecture room V35, Teknikringen 76, (Väg och vatten), Floor 5, KTH main campus
Participating: Professor Dr. Geoffrey Ye Li School of Electrical and Computer Engineering at Georgia Institute of
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of communication systems. In this talk, we introduce our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures.
For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems. At the end of the talk, we provide some potential research topics in the area.
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications.
In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 35,000 times and Thomson Reuters almost every year since 2001 have listed him as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher. He has been an IEEE Fellow since 2006.