R2-D2D: A Novel Deep Learning Based Content-Caching Framework for D2D Networks

The explosive growth of wireless data and traffic, accompanied by the rapid advancements in intelligence and processing power of user equipments (UEs), has paved the way for device-to-device (D2D) communication technology to surface as a promising solution. One major benefit is that users can collaboratively cache and share content to reduce costs associated with backhaul links. In this paper, we explore different approaches to cache content for users in a D2D enabled environment and propose a novel two-stacked approach to achieve a higher cache-hit ratio while leveraging advancements in deep learning. We propose the ‘R2-D2D’ framework, wherein we use Long Short-Term Memory (LSTM) networks stacked with a recently developed omni-scale convolutional neural network (CNN) for making the caching decision. Unlike most previous works, the proposed system model works without any apriori knowledge like file popularity distribution, or any assumptions like stationarity of the environment. Our experiments show that the proposed framework learns well from historical information, obtaining an overall average D2D cache hit ratio of 0.418 when 5000 timesteps of historical information were provided, outperforming a recently proposed neural network collaborative filtering (NCF) framework by approximately 10% to 25%.