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