Spiking Neural Network Decision Feedback Equalization
- URL: http://arxiv.org/abs/2211.04756v2
- Date: Fri, 11 Nov 2022 08:26:22 GMT
- Title: Spiking Neural Network Decision Feedback Equalization
- Authors: Eike-Manuel Bansbach, Alexander von Bank, Laurent Schmalen
- Abstract summary: We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE)
We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels.
The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.
- Score: 70.3497683558609
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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