Content popularity prediction for cache-enabled wireless B5G networks

In this paper, we study the cache prediction problem for mobile edge networks where there exist one base station (BS) and multiple relays. For the proposed mobile edge computing (MEC) network, we propose a cache prediction framework to solve the problem of contents prediction and caching based on neural networks and relay selection, by exploiting users’ history request data and channels between the relays and users. The proposed framework is then trained to learn users’ preferences by using the users’ history requested data, and several caching policies are proposed based on the channel conditions. The cache hit rate and latency are used to measure the performance of the proposed framework. Simulation results demonstrate the effectiveness of the proposed framework, which can maximize the cache hit rate and meanwhile minimize the latency for the considered MEC networks.