Proactive Deployment of Cache-Enabled Aerial Base Stations for Optimized Energy-Delay Cost

Unmanned aerial vehicles (UAVs) via caching have been envisioned as a promising technology for many applications. In cache-enabled drone small cell (DSC) networks, file delivery delay and energy consumption are two critical performance metrics. Firstly, power control is an essential mechanism to mitigate inter-cell interference in DSCs. Secondly, the UAV deployment and content placement are vital for the quality of experience (QoE) of users. In this paper, we propose the proactive cache-enabled drone management (PCDM) framework including mobility prediction model and proactive optimization mechanisms to reduce the energy consumption and file delivery delay cost of the system. We build an accurate mobility prediction model by supervised learning, which uses historical user location to train the model. In the proactive optimization mechanisms, the mobility-aware movement and power control (M
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PC) scheme for DSCs and mobility-aware content placement (MCP) for cache devices are developed. In our simulation results, the proposed method effectively reduces the average energy and delay cost.