Analytics and Machine Learning Powered Wireless Network Optimization and Planning
Ying Li, Djordje Tujkovic, Po-Han Huang

TL;DR
This paper reviews data analytics and machine learning techniques for optimizing and planning wireless networks, addressing challenges of complexity, scale, and dynamic traffic to improve performance and user experience.
Contribution
It introduces comprehensive approaches for monitoring, diagnosing, optimizing, and planning wireless networks using machine learning and data analytics, including metrics derivation and predictive modeling.
Findings
Effective anomaly detection for network metrics
Root cause analysis improves troubleshooting
Predictive metrics enable proactive planning
Abstract
It is important that the wireless network is well optimized and planned, using the limited wireless spectrum resources, to serve the explosively growing traffic and diverse applications needs of end users. Considering the challenges of dynamics and complexity of the wireless systems, and the scale of the networks, it is desirable to have solutions to automatically monitor, analyze, optimize, and plan the network. This article discusses approaches and solutions of data analytics and machine learning powered optimization and planning. The approaches include analyzing some important metrics of performances and experiences, at the lower layers and upper layers of open systems interconnection (OSI) model, as well as deriving a metric of the end user perceived network congestion indicator. The approaches include monitoring and diagnosis such as anomaly detection of the metrics, root cause…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Telecommunications and Broadcasting Technologies
