  In this paper, the problem of joint caching and resource allocation is
investigated for a network of cache-enabled unmanned aerial vehicles (UAVs)
that service wireless ground users over the LTE licensed and unlicensed (LTE-U)
bands. The considered model focuses on users that can access both licensed and
unlicensed bands while receiving contents from either the cache units at the
UAVs directly or via content server-UAV-user links. This problem is formulated
as an optimization problem which jointly incorporates user association,
spectrum allocation, and content caching. To solve this problem, a distributed
algorithm based on the machine learning framework of liquid state machine (LSM)
is proposed. Using the proposed LSM algorithm, the cloud can predict the users'
content request distribution while having only limited information on the
network's and users' states. The proposed algorithm also enables the UAVs to
autonomously choose the optimal resource allocation strategies that maximize
the number of users with stable queues depending on the network states. Based
on the users' association and content request distributions, the optimal
contents that need to be cached at UAVs as well as the optimal resource
allocation are derived. Simulation results using real datasets show that the
proposed approach yields up to 33.3% and 50.3% gains, respectively, in terms of
the number of users that have stable queues compared to two baseline
algorithms: Q-learning with cache and Q-learning without cache. The results
also show that LSM significantly improves the convergence time of up to 33.3%
compared to conventional learning algorithms such as Q-learning.
