Neural Network-based Quantization for Network Automation
This work addresses a computational bottleneck for network management automation, but it is incremental as it builds on an existing algorithm.
The paper tackles the slow training time of the Bounding Sphere Quantization (BSQ) algorithm for network automation by implementing it with neural networks, achieving competitive training speeds.
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.