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20232026
most citedEnergy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding

2 citations · 2 across the 7 of their papers we have counts for

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eess.SP2026

Spiking Neural Network-based Equalization and Demapping in IM/DD Systems: A Comparison

Eike-Manuel Edelmann, Alexander von Bank, Laurent Schmalen

In recent years, artificial neural networks (ANNs) have emerged as the de facto standard for addressing challenging problems in communications engineering that are difficult to sol…

eess.SP2026

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…

eess.SP2024

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…

eess.SP2024

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…

eess.SP2024

Spiking Neural Belief Propagation Decoder for Short Block Length LDPC Codes

Alexander von Bank, Eike-Manuel Edelmann, Sisi Miao +2

Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This paper proposes a novel decoding algorithm fo…

eess.SP2023★ 2 cited

Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding

Alexander von Bank, Eike-Manuel Edelmann, Laurent Schmalen

We propose an energy-efficient equalizer for IM/DD systems based on spiking neural networks. We optimize a neural spike encoding that boosts the equalizer's performance while decre…