  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.
