LEARN TO CACHE: MACHINE LEARNING FOR NETWORK EDGE CACHING IN THE BIG DATA ERA

The unprecedented growth of wireless data traffic not only challenges the design and evolution of the wireless network architecture, but also brings about profound opportunities to drive and improve future networks. Meanwhile, the evolution of communications and computing technologies can make the network edge, such as BSs or UEs, become intelligent and rich in terms of computing and communications capabilities, which intuitively enables big data analytics at the network edge. In this article, we propose to explore big data analytics to advance edge caching capability, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resource in future networks. The learning-based approaches for network edge caching are discussed, where a vast amount of data can be harnessed for content popularity estimation and proactive caching strategy design. An outlook of research directions, challenges, and opportunities is provided and discussed in depth. To validate the proposed solution, a case study and a performance evaluation are presented. Numerical studies show that several gains are achieved by employing learning-based schemes for edge caching.