Spatial-Temporal Content Popularity Prediction in Cache Enabled Cellular Networks

With the development of Internet and mobile communication technology, the mobile network traffic is increasing at exponential rates. Edge caching is a promising technology to reduce network load and content distribution delay. Through content popularity prediction, cache revenue and network per-formance can be improved. This paper proposes a temporal graph convolutional network (TGCN) based content popularity prediction algorithm, which explore the spatial-temporal two-dimensional features in the cellular networks. The proposed TGCN algorithm captures the temporal-dimension dependence from the content request sequence in the base stations (BSs) and the spatial-dimension dependence from different BSs. Then the content request at each BS in the next time cycle is predicted by TGCN. Simulation results show that, compared with the existing algorithms, the proposed algorithm can effectively improve the prediction accuracy of content requests, at least 3%, and improve the cache hit rate of the networks.