5 papers
Spiking Neural Networks for Communication Systems: Encoding Schemes, Learning Algorithms, and Equalization~Techniques
Eike-Manuel Edelmann
Machine learning with artificial neural networks (ANNs), provides solutions for the growing complexity of modern communication systems. This complexity, however, increases power co…
Encoding Optimization for Low-Complexity Spiking Neural Network Equalizers in IM/DD Systems
Eike-Manuel Edelmann, Alexander von Bank, Laurent Schmalen
Neural encoding parameters for spiking neural networks (SNNs) are typically set heuristically. We propose a reinforcement learning-based algorithm to optimize them. Applied to an S…
Spiking Neural Belief Propagation Decoder for LDPC Codes with Small Variable Node Degrees
Alexander von Bank, Eike-Manuel Edelmann, Jonathan Mandelbaum +1
Spiking neural networks (SNNs) promise energy-efficient data processing by imitating the event-based behavior of biological neurons. In previous work, we introduced the enlarge-lik…
Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware
Elias Arnold, Eike-Manuel Edelmann, Alexander von Bank +3
Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditi…
Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications
Laurent Schmalen, Vincent Lauinger, Jonas Ney +5
In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as…