Dieses Bild zeigt Sebastian Dörner

Sebastian Dörner

Herr Dr.-Ing.

Wissenschaftlicher Mitarbeiter
Institut für Nachrichtenübertragung

Kontakt

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. D. Tandler, S. Dörner, M. Gauger, und S. ten Brink, „Deep Reinforcement Learning for mmWave Initial Beam Alignment“, in WSA & SCC 2023; 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding, 2023, S. 1–6 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/10104547
  2. S. Dörner, J. Clausius, S. Cammerer, und S. ten Brink, „Learning Joint Detection, Equalization and Decoding for Short-Packet Communications“, IEEE Transactions on Communications, Bd. 71, Nr. 2, S. 837–850, 2023 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9982554
  3. M. B. Fischer, S. Dörner, S. Cammerer, T. Shimizu, H. Lu, und S. ten Brink, „Adaptive Neural Network-based OFDM Receivers“, in 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), 2022 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9833970
  4. M. B. Fischer, S. Dörner, F. Krieg, S. Cammerer, und S. ten Brink, „Adaptive NN-based OFDM Receivers: Computational Complexity vs. Achievable Performance“, in 2022 56th Asilomar Conference on Signals, Systems, and Computers, 2022, S. 194–199 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/10051898
  5. F. Euchner, N. Süppel, M. Gauger, S. Dörner, und S. ten Brink, „Deep Learning for Uplink CSI-based Downlink Precoding in FDD massive MIMO Evaluated on Indoor Measurements“, in 2022 30th European Signal Processing Conference (EUSIPCO), 2022, S. 653–657 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/abstract/document/9909772
  6. J. Ney u. a., „FPGA-Based Trainable Autoencoder for Communication Systems“, in Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Virtual Event, USA, 2022, S. 154 [Online]. Verfügbar unter: https://dl.acm.org/doi/abs/10.1145/3490422.3502337
  7. F. Euchner, P. Stephan, M. Gauger, S. Dörner, und S. ten Brink, „Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements“, in 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), 2022 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9833925
  8. T. Uhlemann, A. Span, S. Dörner, und S. ten Brink, „Introducing γ-lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication“, in Photonic Networks; 23th ITG-Symposium, 2022, S. 1–8 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9861851
  9. J. Clausius, S. Dörner, S. Cammerer, und S. T. Brink, „On End-to-End Learning of Joint Detection and Decoding for Short-Packet Communications“, in 2022 IEEE Globecom Workshops (GC Wkshps), 2022, S. 377–382 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/10008594
  10. F. Euchner, M. Gauger, S. Dörner, und S. ten Brink, „A Distributed Massive MIMO Channel Sounder for ‚Big CSI Data‘-driven Machine Learning“, in 25th International ITG Workshop on Smart Antennas (WSA 2021), Sophia Antipolis, France, 2021 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9739175
  11. S. Dörner, S. Rottacker, M. Gauger, und S. ten Brink, „Bit-wise Autoencoder for Multiple Antenna Systems“, in 2021 17th International Symposium on Wireless Communication Systems (ISWCS), 2021, S. 1–5 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9562222
  12. J. Clausius, S. Dörner, S. Cammerer, und S. ten Brink, „Serial vs. Parallel Turbo-Autoencoders and Accelerated Training for Learned Channel Codes“, in 2021 11th International Symposium on Topics in Coding (ISTC), 2021, S. 1–5 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9594130
  13. M. B. Fischer u. a., „Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications“, in 2021 IEEE Intelligent Vehicles Symposium (IV), 2021, S. 458–465 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9575620
  14. T. Uhlemann, S. Cammerer, A. Span, S. Dörner, und S. ten Brink, „Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication“, in Photonic Networks 21th ITG-Symposium, 2020, S. 1–8 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9273758
  15. T. Uhlemann, S. Cammerer, A. Span, S. Doerner, und S. ten Brink, „Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication“, in Photonic Networks; 21th ITG-Symposium, 2020, S. 1–8 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/abstract/document/9273758
  16. F. A. Aoudia, S. Cammerer, S. Dörner, M. Stark, J. Hoydis, und S. ten Brink, „Extended Abstract: Deep Learning of the Physical Layer for BICM Systems“, in 2020 IEEE Workshop on Signal Processing Systems (SiPS), 2020, S. 1–1 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9195252
  17. S. Cammerer, F. A. Aoudia, S. Dörner, M. Stark, J. Hoydis, und S. ten Brink, „Trainable Communication Systems: Concepts and Prototype“, IEEE Transactions on Communications, Bd. 68, Nr. 9, S. 5489–5503, Sep. 2020 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9118963
  18. S. Dörner, M. Henninger, S. Cammerer, und S. ten Brink, „WGAN-based Autoencoder Training Over-the-air“, in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020, S. 1–5 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9154335
  19. D. Tandler, S. Dörner, S. Cammerer, und S. ten Brink, „On Recurrent Neural Networks for Sequence-based Processing in Communications“, in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, S. 537–543 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9048728
  20. M. Arnold, S. Dörner, S. Cammerer, J. Hoydis, und 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, S. 1972–1976 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/9048863
  21. M. Widmaier, M. Arnold, S. Dörner, S. Cammerer, und S. ten Brink, „Towards Practical Indoor Positioning Based on Massive MIMO Systems“, in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019, S. 1–6 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/8891273
  22. S. Dörner, S. Cammerer, J. Hoydis, und S. ten Brink, „Deep Learning Based Communication Over the Air“, IEEE Journal of Selected Topics in Signal Processing, Bd. 12, Nr. 1, S. 132–143, Feb. 2018 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/8214233
  23. S. Cammerer, S. Dörner, J. Hoydis, und S. ten Brink, „End-to-end Learning for Physical Layer Communications“, in International Zurich Seminar on Information and Communication (IZS 2018), 2018, S. 51–52 [Online]. Verfügbar unter: https://doi.org/10.3929/ethz-b-000245055
  24. A. Felix, S. Cammerer, S. Dörner, J. Hoydis, und 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), 2018, S. 1–5 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/8445920
  25. M. Arnold, S. Dörner, S. Cammerer, und 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), 2018, S. 1–5 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/8446013
  26. S. Schibisch, S. Cammerer, S. Dörner, J. Hoydis, und 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), 2018, S. 1–5 [Online]. Verfügbar unter: https://ieeexplore.ieee.org/document/8491189
  27. S. Dörner, S. Cammerer, J. Hoydis, und S. ten Brink, „On deep learning-based communication over the air“, in 2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017, S. 1791–1795 [Online]. Verfügbar unter: 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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