2019 Best Paper Award, University of Stuttgart

January 18, 2019 /

2019 Best Paper Award, University of Stuttgart

2019 Best Paper Award of the Faculty of Computer Science, Electrical Engineering and Information Technology, University of Stuttgart.

INÜ Researchers Sebastian Dörner, Sebastian Cammerer, Jakob Hoydis (Nokia Bell Labs) and Stephan ten Brink receive the 2019 Best Paper Award of the Faculty of Computer Science, Electrical Engineering and Information Technology, University of Stuttgart. The award was handed over at the University Science Day on Jan. 18, 2019 and recognizes the landmark paper on "Deep Learning Based Communication over the Air", IEEE Journal of Selected Topics in Signal Processing, Year: Feb. 2018, Volume: 12, Issue: 1, Pages: 132 – 143 (see IEEE Xplorer for downloading the paper).

 

About the paper:

End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and  receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf SDRs and open-source DL software libraries. We extend the existing ideas towards continuous data transmission which eases their current restriction to short block lengths but also entails the issue of receiver synchronization. We overcome this problem by introducing a frame synchronization module based on another NN. A comparison of the BLER performance of the “learned” system with that of a practical baseline shows competitive performance close to 1dB, even without extensive hyperparameter tuning. We identify several practical challenges of training such a system over actual channels, in particular the missing channel gradient, and propose a two-step learning procedure based on the idea of transfer learning that circumvents this issue.

2019 Best Paper Award, University of Stuttgart
2019 Best Paper Award, University of Stuttgart