PSAC: Proactive Sequence-Aware Content Caching via Deep Learning at the Network Edge
Yin Zhang⋆, Yujie Li, Ranran Wang, Jianmin Lu, Xiao Ma, Meikang Qiu
Abstract
Compared with traditional ineffective methods, such as acquiring more spectrum and deploying more base stations, edge caching is a highly promising solution for increased data flow needs and has attracted considerable attention. However, owing to the lack of careful consideration of cached data, existing related methods neither reduce network load nor improve the quality of experience. In this study, we propose a proactive sequence-aware content caching strategy (PSAC). Specifically, for general content at the network edge and content with sequential features, PSAC_gen (based on a convolutional neural network) and PSAC_seq (based on an attention mechanism that can automatically capture sequential features), respectively, are proposed to implement proactive caching. Experiments demonstrate that the proposed deep learning content caching method can effectively improve user experience and reduce network load.