Deep Learning-Enabled Joint Edge Content Caching and Power Allocation Strategy in Wireless Networks

Edge content caching has emerged as a promising solution against network latency by pre-caching popular contents at the edge of networks. However, the edge content caching and wireless resource allocation are intertwined with each other. The content downloading latency depends not only on the edge content caching strategy, but also highly on the wireless resource allocation strategy. To cope with them as a whole, this work aims at jointly optimizing the edge content caching and power allocation to minimize the content downloading latency. In doing so, a joint edge content caching and power allocation problem is formulated. But, this problem is NP-hard and difficult to be solved. Solving it with the traditional approaches will cause quite long computational delay, which cannot meet the real-time requirements of resource scheduling. To address this challenge, a novel deep learning (DL)-enabled joint edge content caching and power allocation framework is proposed. In particular, the formulated problem is firstly transformed into a classification problem in DL field. After that, the convolutional long short-term memory networks (ConvLSTM) are utilized to capture the temporal-spatial features of content requests, and the fully-connected networks (FC) are introduced to explore the users' location differences. Finally, the temporal-spatial features of content requests and the location differences are fused to make intelligent decisions for joint edge content caching and power allocation. Simulation results show that the proposed joint edge content caching and power allocation strategy outperforms the state-of-the-art baselines at about 2.3% in terms of the average content downloading latency while guaranteeing real-time resource scheduling.