Intelligent Caching in UAV-Aided Networks

Deployment of unmanned aerial vehicles (UAVs) as flying base stations to provide specific geographical area with air-to-ground wireless communications is expected to increase dramatically in the coming decades, owing to its flexibility, mobility and autonomy. Moreover, mobile edge computing (MEC) promises significant reduction in latency by caching popular contents at the mobile edge. In this paper, we propose a method to apply mobile edge caching on UAVs in wireless communication systems. By investigating the user request preference with the aid of latent Dirichlet allocation (LDA), the caching strategy can be optimized. In the proposed system, we consider the design of intelligent caching strategies when a number of UAVs are deployed to serve ground users, where each UAV has a limited storage capacity for caching useful user contents. We use LDA to extract the user request preferences in order to intelligently cache data in the UAVs, while we utilize
$k$
-means clustering to classify users and to help deploy UAVs. Besides, we consider three user-UAV association criteria, namely the user received signal to noise ratio (SNR), user preferences and the delay. Our simulation results show that, when compared to random caching, the average caching efficiency could be significantly improved from
$\text{50}\%$
to
$\text{70}\%$
, while the latency of our proposed system can also be greatly reduced.