# Spiking Neural Network Decision Feedback Equalization

arXiv: https://arxiv.org/abs/2211.04756 · HTML: https://mlsanity.com/paper/2211.04756/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://mlsanity.com/paper/2201.11063/) (2022)
- [Surrogate gradients for analog neuromorphic computing](https://mlsanity.com/paper/2006.07239/) (2020)
- [Brain-Inspired Learning on Neuromorphic Substrates](https://mlsanity.com/paper/2010.11931/) (2020)
- [Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2103.08212/) (2021)
- [Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck](https://mlsanity.com/paper/2106.01177/) (2021)
- [End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence](https://mlsanity.com/paper/2009.01527/) (2020)
- [Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders](https://mlsanity.com/paper/2204.11776/) (2022)
- [Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls](https://mlsanity.com/paper/2109.14942/) (2021)
- [Spiking Neural Network Equalization for IM/DD Optical Communication](https://mlsanity.com/paper/2205.04263/) (2022)
- [Spiking Neural Network Equalization on Neuromorphic Hardware for IM/DD Optical Communication](https://mlsanity.com/paper/2206.00401/) (2022)

## Built on by (most cited first, 3 of 3)

- [Spiking Neural Network Decision Feedback Equalization for IM/DD Systems](https://mlsanity.com/paper/2304.14152/) (2023)
- [Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding](https://mlsanity.com/paper/2312.12909/) (2023)
- [Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications](https://mlsanity.com/paper/2411.10101/) (2024)

## Abstract (arXiv)

In the past years, artificial neural networks (ANNs) have become the de-facto standard to solve tasks in communications engineering that are difficult to solve with traditional methods. In parallel, the artificial intelligence community drives its research to biology-inspired, brain-like spiking neural networks (SNNs), which promise extremely energy-efficient computing. In this paper, we investigate the use of SNNs in the context of channel equalization for ultra-low complexity receivers. We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE). For conversion of real-world data into spike signals we introduce a novel ternary encoding and compare it with traditional log-scale encoding. We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels. We highlight that mainly the conversion of the channel output to spikes introduces a small performance penalty. The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.