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

By M. G. Kibria, K. Nguyen, G. Villardi, Ou Zhao, K. Ishizu, F. Kojima. IEEE Access, 2017

387 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/69dbd0f1e4d7d804dea0be176fd3adc2c7cd8bb3

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Citations in the corpus

A Survey on Green 6G Network: Architecture and Technologies (2019, background): “complex and heterogeneous system because of diversified service requirements and explosively growing number of connected devices, a novel AI paradigm of self-aware, selfadaptive, self-interpretive and prescriptive networking is much needed [38].”
6G Wireless Communications: Vision and Potential Techniques (2019, background): “[11] M.”
Artificial-Intelligence-Enabled Intelligent 6G Networks (2019, methodology): “Artificial intelligence (AI) [7, 9], with strong learning ability, powerful reasoning ability and intelligent recognition ability, allows the architecture of 6G networks to learn and adapt itself to support diverse services accordingly without human intervention.”
Security for 5G and Beyond (2019, background): “As described in [357], AI has the poten-”
Blockchain for 5G and Beyond Networks: A State of the Art Survey (2019, background): “2) Blockchain for big data in 5G: In the age of data explosion, big data becomes a hot research topic in 5G [314].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “tems and millimeter-Wave (mmWave) spectrum exploiting high spatial resolution and multi-gigahertz bandwidth, respectively, are believed to have an important role in addressing the capacity demands of future communication networks [12, 22].”