Eike-Manuel Edelmann, M.Sc.

Eike-Manuel Edelmann, M.Sc.

Research Topics

  • Analysis of neuromorphic hardware and development of algorithms in context of communication engineering
  • Resource allocation for mobile communication using deep reinforcement learning

Teaching

  • Tutorial Nachrichtentechnik I (since WS 20/21)
  • Praktikum Nachrichtentechnik (since SS 20)

Current Theses

Theses

Open Theses

Title Supervisor Type
Ressourcenzuweisung mittels Spiking Neural Network-basierten Deep Reinforcement Learning Algorithmen Eike-Manuel Edelmann MA

Theses In Progress

Title Supervisor Type

There are currently no theses in progress.

Full List of Publications

Journal papers

  1. A. von Bank, E.-M. Edelmann, S. Miao, J. Mandelbaum and L. Schmalen, "Spiking neural belief propagation decoder for short block length LDPC codes," IEEE Commun. Lett., vol. 29, no. 1, pp. 45-49, Jan. 2025, https://arxiv.org/abs/2410.11543
    [ Cite ]  [ Available here ]  [ Code ]

Conference papers

  1. E.-M. Edelmann, A. von Bank and L. Schmalen, "Encoding optimization for low-complexity spiking neural network equalizers in IM/DD systems," Proc. Eur. Conf. Opt. Commun. (ECOC), Copenhagen, Denmark, Sep. 2025
    [ Cite ]  
  2. L. Schmalen, V. Lauinger, J. Ney, N. Wehn, P. Matalla, S. Randel, A. von Bank and E. M. Edelmann, "Recent advances on machine learning-aided DSP for short-reach and long-haul optical communications," Proc. Opt. Fiber Commun. Conf. (OFC), San Francisco, CA, USA, Mar. 2025, invited presentation, https://arxiv.org/abs/2411.10101
    [ Cite ]  [ Presentation ]  
  3. A. von Bank, E.-M. Edelmann, J. Mandelbaum and L. Schmalen, "Spiking neural belief propagation decoder for LDPC codes with small variable node degrees," Proc. Intl. ITG Conf. on Systems, Communications, and Coding (SCC), Karlsruhe, Germany, Mar. 2025, https://arxiv.org/abs/2412.15897
    [ Cite ]  
  4. E. Arnold, E.-M. Edelmann, A. von Bank, E. Müller, L. Schmalen and J. Schemmel, "Short-reach optical communications: a real-world task for neuromorphic hardware," Proc. Neuro Inspired Computational Elements Conference (NICE), Heidelberg, Germany, Mar. 2025, https://arxiv.org/abs/2412.03129
    [ Cite ]  
  5. A. von Bank, E. Edelmann and L. Schmalen, "Energy-efficient spiking neural network equalization for IM/DD systems with optimized neural encoding," Proc. Opt. Fiber Commun. Conf. (OFC), San Diego, CA, USA, Mar. 2024, https://arxiv.org/abs/2312.12909
    [ Cite ]  [ Code ]
  6. A. von Bank, E. Edelmann and L. Schmalen, "Spiking neural network decision feedback equalization for IM/DD systems," Proc. Advanced Photonic Congress: Signal Processing in Photonic Communications (SPPCom), Busan, South Korea, Jul. 2023, https://arxiv.org/abs/2304.14152
    [ Cite ]  [ Presentation ]  [ Code ]
  7. E. M. Bansbach, A. von Bank and L. Schmalen, "Spiking neural network decision feedback equalization," Proc. Intl. Workshop on Smart Antennas and ITG Conf. on Systems, Communications, and Coding (WSA-SCC), Braunschweig, Germany, Feb. 2023, https://arxiv.org/abs/2211.04756
    [ Cite ]  [ Presentation ]  
  8. E. M. Bansbach, Y. Kiyak and L. Schmalen, "Deep reinforcement learning for uplink multi-carrier non-orthogonal multiple access resource allocation using buffer state information," Proc. European Wireless, Dresden, Germany, Sep. 2022, https://arxiv.org/abs/2208.14689
    [ Cite ]  [ Presentation ]  
  9. E. M. Bansbach, V. Eliachevitch and L. Schmalen, "Deep reinforcement learning for wireless resource allocation using buffer state information," Proc. IEEE Global Commun. Conf. (GLOBECOM), Madrid, Spain, Dec. 2021, preprint available at https://arxiv.org/abs/2108.12198
    [ Cite ]  [ Presentation ]