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In this paper, the problem of proactive deployment of cache-enabled unmannedaerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) ofwireless devices in a cloud radio access network (CRAN) is studied. In theconsidered model, the network can leverage human-centric information such asusers' visited locations, requested contents, gender, job, and device type topredict the content request distribution and mobility pattern of each user.Then, given these behavior predictions, the proposed approach seeks to find theuser-UAV associations, the optimal UAVs' locations, and the contents to cacheat UAVs. This problem is formulated as an optimization problem whose goal is tomaximize the users' QoE while minimizing the transmit power used by the UAVs.To solve this problem, a novel algorithm based on the machine learningframework of conceptor-based echo state networks (ESNs) is proposed. UsingESNs, the network can effectively predict each user's content requestdistribution and its mobility pattern when limited information on the states ofusers and the network is available. Based on the predictions of the users'content request distribution and their mobility patterns, we derive the optimaluser-UAV association, optimal locations of the UAVs as well as the content tocache at UAVs. Simulation results using real pedestrian mobility patterns fromBUPT and actual content transmission data from Youku show that the proposedalgorithm can yield 40% and 61% gains, respectively, in terms of the averagetransmit power and the percentage of the users with satisfied QoE compared to abenchmark algorithm without caching and a benchmark solution without UAVs.

Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience