2 research outputs found
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Recent years have seen rapid deployment of mobile computing and Internet of
Things (IoT) networks, which can be mostly attributed to the increasing
communication and sensing capabilities of wireless systems. Big data analysis,
pervasive computing, and eventually artificial intelligence (AI) are envisaged
to be deployed on top of the IoT and create a new world featured by data-driven
AI. In this context, a novel paradigm of merging AI and wireless
communications, called Wireless AI that pushes AI frontiers to the network
edge, is widely regarded as a key enabler for future intelligent network
evolution. To this end, we present a comprehensive survey of the latest studies
in wireless AI from the data-driven perspective. Specifically, we first propose
a novel Wireless AI architecture that covers five key data-driven AI themes in
wireless networks, including Sensing AI, Network Device AI, Access AI, User
Device AI and Data-provenance AI. Then, for each data-driven AI theme, we
present an overview on the use of AI approaches to solve the emerging
data-related problems and show how AI can empower wireless network
functionalities. Particularly, compared to the other related survey papers, we
provide an in-depth discussion on the Wireless AI applications in various
data-driven domains wherein AI proves extremely useful for wireless network
design and optimization. Finally, research challenges and future visions are
also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page