Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

TLDR

Next‑generation wireless networks are becoming highly complex due to diverse services, heterogeneous devices, and infrastructure, making traditional reactive, centrally‑managed approaches insufficient for efficient resource use. The paper proposes a data‑driven paradigm that uses advanced analytics, machine learning, and AI to enable proactive, self‑aware, self‑adaptive, and predictive next‑generation wireless networks. The authors outline how operators can leverage data analytics, machine learning, and AI—drawing on diverse data sources—to design and optimize network schemes that support self‑aware, self‑adaptive, and proactive behavior. Exploiting big data enables smarter, more intelligent networks that operate more efficiently and cost‑effectively.

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 network operators 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 regarding operation and optimization cost effectively. A novel paradigm of proactive, self-aware, self-adaptive, and predictive networking is much needed. The network operators have access to large amounts of data, especially from the network and the subscribers. Systematic exploitation of the big data dramatically helps in making the system smart, intelligent, and facilitates efficient as well as cost-effective operation and optimization. We envision data-driven next-generation wireless networks, where the network operators employ advanced data analytics, machine learning (ML), and artificial intelligence. We discuss the data sources and strong drivers for the adoption of the data analytics, and the role of ML, artificial intelligence in making the system intelligent regarding being self-aware, self-adaptive, proactive and prescriptive. A set of network design and optimization schemes are presented concerning data analytics. This paper concludes with a discussion of challenges and the benefits of adopting big data analytics, ML, and artificial intelligence in the next-generation communication systems.