Investigation of Spiking Neural Networks for Modulation Recognition using Spike-Timing-Dependent Plasticity

Spiking neural networks (SNNs) operating on neuromorphic hardware can enable cognitive functionality with relatively low power consumption as compared to other artificial neural network implementations, making it ideally suited for resource-constrained space platforms such as CubeSats. The objective...

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Bibliographic Details
Published in2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW) pp. 1 - 5
Main Authors Knoblock, Eric J., Bahrami, Hamid R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Computer architecture
CubeSat
CubeSats
machine learning
Mathematical model
Membrane potentials
Modulation
modulation recognition
neuromorphic platforms
Neuromorphics
Neurons
space communications
spike-timing-dependent plasticity
spiking neural networks
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Summary:Spiking neural networks (SNNs) operating on neuromorphic hardware can enable cognitive functionality with relatively low power consumption as compared to other artificial neural network implementations, making it ideally suited for resource-constrained space platforms such as CubeSats. The objective of this study is to investigate the implementation of a modulation recognition capability using SNNs, which may eventually be applied to neuromorphic hardware for implementation. This preliminary analysis uses a software simulation approach with an unsupervised learning algorithm based on spike-timing-dependent plasticity for classification of digital modulation constellation patterns. This modulation recognition capability can provide enhanced situational awareness for a space platform and facilitate additional high-level cognitive functionality that can be investigated in future studies.
DOI:10.1109/CCAAW.2019.8904911