Big Data Analytics, Machine Learning and Artificial Intelligence in Next-Generation Wireless Networks
Mirza Golam Kibria, Kien Nguyen, Gabriel Porto Villardi, Ou Zhao,, Kentaro Ishizu, Fumihide Kojima

TL;DR
This paper explores how big data analytics, machine learning, and AI can transform next-generation wireless networks into smart, self-aware, and adaptive systems for improved efficiency and resource management.
Contribution
It introduces a data-driven network model leveraging advanced analytics and AI to enable proactive, self-adaptive, and optimized wireless network operations.
Findings
Enhanced network intelligence through data analytics
Improved resource optimization strategies
Discussion of challenges and benefits
Abstract
The next-generation wireless networks are evolving into very complex systems because of the very diversified service requirements, heterogeneity in applications, devices, and networks. The mobile network operators (MNOs) need to make the best use of the available resources, for example, power, spectrum, as well as infrastructures. Traditional networking approaches, i.e., reactive, centrally-managed, one-size-fits-all approaches and conventional data analysis tools that have limited capability (space and time) are not competent anymore and cannot satisfy and serve that future complex networks in terms of operation and optimization in a cost-effective way. A novel paradigm of proactive, self-aware, self- adaptive and predictive networking is much needed. The MNOs have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data…
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Taxonomy
TopicsWireless Signal Modulation Classification · IoT Networks and Protocols · Advanced MIMO Systems Optimization
