A Survey on Machine Learning Based Proactive Caching

A Survey on Machine Learning Based Proactive Caching

Stephen Anokye1,2, Mohammed S. Abegaz1,3 and SUN Guolin1

(1. University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;
2. University of Mines and Technology, Tarkwa 237, Ghana;
3. Dilla University, Dilla, Ethiopia)

Abstract: The world today is experiencing an enormous increase in data traffic, coupled with demand for greater quality of experience (QoE) and performance. Increasing mobile traffic leads to congestion of backhaul networks. One promising solution to this problem is the mobile edge network (MEN) and consequently mobile edge caching. In this paper, a survey of mobile edge caching using machine learning is explored. Even though a lot of work and surveys have been conducted on mobile edge caching, our efforts in this paper are rather focused on the survey on machine learning based mobile edge caching. Issues affecting edge caching, such as caching entities, caching policies and caching algorithms, are discussed. The machine learning algorithms applied to edge caching are reviewed followed by a discussion on the challenges and future works in this field. This survey shows that edge caching can reduce delay and subsequently the backhaul traffic of the network; most caching is conducted at the small base stations (SBSs) and caching at unmanned aerial vehicles (UAVs) is recently used to accommodate mobile users who dissociate from the SBSs. This survey also demonstrates that machine learning approach is the state of the art and reinforcement learning is predominant.
Keywords: mobile edge caching; reinforcement learning; unmanned aerial vehicle