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Artificial Intelligence-Powered Mobile Edge Computing-Based Anomaly Detection in Cellular Networks
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DOI：10.1109/TII.2019.2953201.png)
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Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. State-of-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, in this article we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that preprocesses raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of Things environment.
Keyword:
Big data analytics
call detail record
convolutional neural networks
deep learning
self-organizing networks
self-healing networks
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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分享收藏A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks应用于自组织蜂窝网络的机器学习技术综述
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