Page 131 - First special issue on The impact of Artificial Intelligence on communication networks and services

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MACHINE INTELLIGENCE TECHNIQUES FOR NEXT-GENERATION
CONTEXT-AWARE WIRELESS NETWORKS

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Abstract – Next generation wireless networks (i.e., 5G and beyond), which will be extremely dynamic
and complex due to the ultra-dense deployment of heterogeneous networks (HetNets), pose many critical
challenges for network planning, operation, management and troubleshooting. At the same time, the
generation and consumption of wireless data are becoming increasingly distributed with an ongoing
paradigm shift from people-centric to machine-oriented communications, making the operation of future
wireless networks even more complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with improved context
awareness becomes extremely important. In this regard, the emerging fog (edge) computing architecture
aiming to distribute computing, storage, control, communication, and networking functions closer to
end users, has a great potential for enabling efficient operation of future wireless networks. These
promising architectures make the adoption of artificial intelligence (AI) principles, which incorporate
learning, reasoning and decision-making mechanisms, natural choices for designing a tightly integrated
network. To this end, this article provides a comprehensive survey on the utilization of AI integrating
machine learning, data analytics and natural language processing (NLP) techniques for enhancing the
efficiency of wireless network operation. In particular, we provide comprehensive discussion on the
utilization of these techniques for efficient data acquisition, knowledge discovery, network planning,
operation and management of next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.

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