Theoretische Grundlagen der Kommunikationstechnik

Invitation To A Talk by Prof. Dr-Ing. Stephan ten Brink, University Stuttgart, Germany

Time15 December 2017, 03:00 p.m.  
LocationRoom MAR 0.007, Marchstr. 23, 10587 Berlin  
TitleOn Deep Learning-Based Communication Over the Air  


We demonstrate an over-the-air communications system which is solely based on deep neural networks and has so far been only validated by computer simulations for block-based transmissions. Transmitter and receiver can be jointly trained end-to-end for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). We demonstrate that it is possible to build and train such a system using off-the-shelf software-defined radios (SDRs) and open-source deep learning software libraries. A comparison of the BLER performance of the “learned” system against that of a practical baseline shows competitive performance. We identify several practical challenges of training such a system over-the-air, in particular the missing channel gradient, and propose a learning procedure that circumvents this issue. The talk is about the paper "On Deep Learning-Based Communication Over the Air“, S. Dörner, S. Cammerer, J. Hoydis, S. ten Brink, presented at Asilomar 2017



Stephan ten Brink has been a faculty member at the University of Stuttgart, Germany, since July 2013, where he is head of the Institute of Telecommunications. From 1995 to 1997 and 2000 to 2003, Dr. ten Brink was with Bell Laboratories in Holmdel, New Jersey, conducting research on multiple antenna systems. From July 2003 to March 2010, he was with Realtek Semiconductor Corp., Irvine, California, as Director of the wireless ASIC department, developing WLAN and UWB single chip MAC/PHY CMOS solutions. In April 2010 he returned to Bell Laboratories as Department Head of the Wireless Physical Layer Research Department in Stuttgart, Germany. Dr. ten Brink is a recipient and co-recipient of several awards, including the Vodafone Innovation Award, the IEEE Stephen O. Rice Paper Prize and the IEEE Leonard G. Abraham Paper Prize for contributions to channel coding and signal detection for multiple-antenna systems. He is best known for his work on iterative decoding (EXIT charts) and MIMO communications (soft sphere detection, massive MIMO).