Dieses Bild zeigt  Sebastian Dörner

Herr M. Sc.

Sebastian Dörner

Wissenschaftlicher Mitarbeiter
Institut für Nachrichtenübertragung

Kontakt

+49 711 685-67945

Pfaffenwaldring 47
70569 Stuttgart
Deutschland
Raum: 2.333 or WebEx

Fachgebiet

Deep Learning for Communications

The huge success of deep learning (DL) and neural networks (NNs), mainly in the fields of computer vision and speech processing, has recently triggered further exploration of DL for communications. The potential applications span from trainable channel decoders, NN-based multiple-input multiple-output (MIMO) detectors and user position estimation based on channel state information up to communication systems that inherently learn to communicate, so called autoencoder communication systems.

Since the first publication of autoencoder-based communications by O'Shea and Hoydis, the vision of end-to-end training of communication systems has attracted an impressive amount of follow-up work. Thereby, end-to-end learning has found its entry into virtually any field of today’s communications research – in the wireless and optical domain, but also emerging domains, like the molecular channel. Although autoencoder-based communication promises a framework that operates over any channel, for practical deployment the missing channel gradient prevents joint end-to-end training of transmitter and receiver. To overcome this major obstacle of end-to-end training, several approaches (ranging from model based to GAN-based based training schemes) have been proposed and investigated at our institute.

We have shown the practicability and universality of the autoencoder framework in several publicatoins, including the demonstration of the world’s first communication system that solely consists of NN-based signal processing at Bell Labs Future X Days in Paris 2017.

The main goal of my research is the combination of classical algorithms in communications with trainable components and deep learning techniques, to improve state of the are baselines (neural network augmented algorithms), wherever possible. While classical solutions are often optimal within their expected operational scope, the idea is to improve them with NNs to either achieve a wider range of operation or combine certain processing steps that are classically done separately with a known performance loss. With the autoencoder framework being the most radical approach, as is aims on replacing most classical signal processing in transmitter and receiver by a single NN.

  1. F. A. Aoudia, S. Cammerer, S. Dörner, M. Stark, J. Hoydis, and S. ten Brink, “Extended Abstract: Deep Learning of the Physical Layer for BICM Systems,” in 2020 IEEE Workshop on Signal Processing Systems (SiPS), pp. 1–1 [Online]. Available: https://ieeexplore.ieee.org/document/9195252
  2. S. Cammerer, F. A. Aoudia, S. Dörner, M. Stark, J. Hoydis, and S. ten Brink, “Trainable Communication Systems: Concepts and Prototype,” IEEE Transactions on Communications, vol. 68, no. 9, pp. 5489–5503 [Online]. Available: https://ieeexplore.ieee.org/document/9118963
  3. S. Dörner, M. Henninger, S. Cammerer, and S. ten Brink, “WGAN-based Autoencoder Training Over-the-air.” 2020 [Online]. Available: https://arxiv.org/abs/2003.02744
  4. D. Tandler, S. Dörner, S. Cammerer, and S. ten Brink, “On Recurrent Neural Networks for Sequence-based Processing in Communications,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 537–543 [Online]. Available: https://ieeexplore.ieee.org/document/9048728
  5. M. Arnold, S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink, “Towards Practical FDD Massive MIMO: CSI Extrapolation Driven by Deep Learning and Actual Channel Measurements,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 1972–1976 [Online]. Available: https://ieeexplore.ieee.org/document/9048863
  6. M. Widmaier, M. Arnold, S. Dörner, S. Cammerer, and S. ten Brink, “Towards Practical Indoor Positioning Based on Massive MIMO Systems,” in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/8891273
  7. S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink, “Deep Learning Based Communication Over the Air,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 132–143 [Online]. Available: https://ieeexplore.ieee.org/document/8214233
  8. S. Cammerer, S. Dörner, J. Hoydis, and S. ten Brink, “End-to-end Learning for Physical Layer Communications,” International Zurich Seminar on Information and Communication (IZS 2018), pp. 51–52 [Online]. Available: https://doi.org/10.3929/ethz-b-000245055
  9. A. Felix, S. Cammerer, S. Dörner, J. Hoydis, and S. ten Brink, “OFDM-Autoencoder for End-to-End Learning of Communications Systems,” in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8445920
  10. M. Arnold, S. Dörner, S. Cammerer, and S. ten Brink, “On Deep Learning-Based Massive MIMO Indoor User Localization,” in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8446013
  11. S. Schibisch, S. Cammerer, S. Dörner, J. Hoydis, and S. ten Brink, “Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes,” in 2018 15th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8491189
  12. S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink, “On deep learning-based communication over the air,” in 2017 51st Asilomar Conference on Signals, Systems, and Computers, pp. 1791–1795 [Online]. Available: https://ieeexplore.ieee.org/document/8335670

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Sebastian Dörner (S’18) received the B.Sc. and M.Sc. degrees in electrical engineering and information technology in 2014 and 2017, respectively, from the University of Stuttgart, Germany, where he has been a member of research staff with the Institute of Telecommunications since April 2017 and is currently working toward the Ph.D. degree. His main research topic is applying machine learning techniques to different areas of communication systems. Further research interests include modulation, parallelized GPU computing and signal processing, channel coding and SDRs. He is recipient of the IEEE SPS Young Author Best Paper Award 2019, the University of Stuttgart’s Best Paper Award 2018, the Dr. Gunter Woysch Award 2017 for his master thesis and third prize winner of the Nokia Bell Labs Prize 2019.

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