AI for Large-Scale Communication Networks
Krithikaa Venket, R. Adline Freeda, Frank Vijay · Advances in wireless technologies and telecommunication book series · 2024
There are techniques representing data mining, information theory and statistical analysis that can bring machine learning capabilities into the analyses of complex networks. The network analysis projects that can be performed by machine learning are the anomaly detection, prediction of problems and Root Cause Analysis for each. ML turned out to be a powerful tool for analysis and management of large scale networks such as social, communication or transportation network. At large-scale networks, machine learning supports in discovering sophisticated patterns, forecasting of net behaviors the performance optimization and anomaly detection. The roles involve using algorithms that learn from or make predictions/decisions based on network data. This may mean understanding network structure, predicted futures, optimization in the use of resources, anomaly detection, and performance improvement. Machine Learning for Network Analysis, or MLNA in short, is a subarea of machine learning interested in developing algorithms and techniques aimed at analysing, comprehending, and predicting complex behaviors in large-scale networks. MLNA brought together ideas from machine learning, graph theory, and network science to gain insights from network data. You can picture any network as a graph, with entities as nodes or vertices, and the connections or interactions between entities as edges or links. This study also looked at how to analyze network structure using ML, ML-based ways to spot unusual patterns in networks how to make ML network analysis work better and faster, and how to use ML network analysis to create visual representations and understand what they mean.