When Machine Learning Meets Big Data: A Wireless Communication Perspective
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We have witnessed an exponential growth in commercial data services, which has lead to the big data's era. Machine learning, as one of the most promising artificial intelligence tools of analyzing the deluge of data, has been invoked in many research areas both in academia and industry. The aim of this article is twin-fold. Firstly, we review big data analysis and machine learning, along with their potential applications in wireless networks. The second goal is to invoke big data analysis to predict the requirements of mobile users and to exploit it for improving the performance of wireless networks. More particularly, a unified big data aided machine learning framework is proposed, which consists of feature extraction, data modeling and prediction/online refinement. The main benefits of the proposed framework are that by relying on big data which reflects the real requirements of users, we can refine the motivation, problem formulations, and methodology of powerful machine learning algorithms in the context of wireless networks. To characterize the efficiency of the proposed framework, a pair of practical case studies are provided: 1) To predict the positioning of drone-mounted areal base stations (BSs) deployments according to specific tele-traffic requirements. 2) To predict the content caching requirements of BSs according to the users' preferences. Finally, open research opportunities are identified for motivating future investigations.
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