Machine Learning and Deep Learning Algorithms for Network Data Analytics Function in 5G Cellular Networks
Ale Pavani, A. Kathirvel
Abstract
Machine learning and deep learning algorithms have the potential to revolutionize network data analytics in 5G cellular networks. With the increase in the number of connected devices and the explosion of data generated by these devices, traditional methods of network data analytics are becoming increasingly inadequate. However, this function faces several challenges, including managing massive amounts of data, processing data in real-time, addressing privacy concerns, integrating with other network systems, and requiring skilled professionals. Overcoming these challenges will enable the network data analytics function to achieve its objectives and improve the overall performance and reliability of 5G cellular networks. Machine learning and deep learning algorithms enable the automatic identification of patterns and insights in large volumes of data, thereby facilitating real-time decision-making and proactive network management. These algorithms can be used for a wide range of network data analytics functions, including network optimization, anomaly detection, prediction of network failures, and dynamic spectrum management. Deep learning algorithms have shown significant promise in processing unstructured network data such as network logs, video, and images. As the 5G network continues to grow and evolve, the use of machine learning and deep learning algorithms in network data analytics is likely to become increasingly essential for ensuring network efficiency, reliability, and security.