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arXiv · 2103.04764

Neural Network-based Quantization for Network Automation

Marton Kajo·Stephen S. Mwanje·Benedek Schultz·Georg Carle
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Abstract

Deep Learning methods have been adopted in mobile networks, especially for network management automation where they provide means for advanced machine cognition. Deep learning methods utilize cutting-edge hardware and software tools, allowing complex cognitive algorithms to be developed. In a recent paper, we introduced the Bounding Sphere Quantization (BSQ) algorithm, a modification of the k-Means algorithm, that was shown to create better quantizations for certain network management use-cases, such as anomaly detection. However, BSQ required a significantly longer time to train than k-Means, a challenge which can be overcome with a neural network-based implementation. In this paper, we present such an implementation of BSQ that utilizes state-of-the-art deep learning tools to achieve a competitive training speed.

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Marton Kajo, Stephen S. Mwanje, Benedek Schultz, Georg Carle. 2021-03-04. Neural Network-based Quantization for Network Automation. https://arxiv.org/abs/2103.04764

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