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.
@article{arxiv.2103.04764,
title = {Neural Network-based Quantization for Network Automation},
author = {Marton Kajo and Stephen S. Mwanje and Benedek Schultz and Georg Carle},
journal= {arXiv preprint arXiv:2103.04764},
year = {2021}
}