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.

本文在通信工程中对脑神经元类似的尖峰神经网络（SNN）进行了研究，提出了一种类似于决策反馈均衡器（DFE）的基于 SNN 的均衡器，并展示了其相比于传统的线性均衡器在三个典型信道上明显的优化效果，为实现低功耗的通信系统提供了可行方案。