High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification

• URL: http://arxiv.org/abs/2010.10717v1
• Date: Wed, 21 Oct 2020 02:26:24 GMT
• Title: High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification
• Authors: Jakob Krzyston, Rajib Bhattacharjea, Andrew Stark
• Abstract summary: We claim state of the art performance by enabling high-capacity architectures containing residual and/or dense connections to compute complex-valued convolutions. We show statistically significant improvements in all networks with complex convolutions for I/Q modulation classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: I/Q modulation classification is a unique pattern recognition problem as the data for each class varies in quality, quantified by signal to noise ratio (SNR), and has structure in the complex-plane. Previous work shows treating these samples as complex-valued signals and computing complex-valued convolutions within deep learning frameworks significantly increases the performance over comparable shallow CNN architectures. In this work, we claim state of the art performance by enabling high-capacity architectures containing residual and/or dense connections to compute complex-valued convolutions, with peak classification accuracy of 92.4% on a benchmark classification problem, the RadioML 2016.10a dataset. We show statistically significant improvements in all networks with complex convolutions for I/Q modulation classification. Complexity and inference speed analyses show models with complex convolutions substantially outperform architectures with a comparable number of parameters and comparable speed by over 10% in each case.

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