  Coded caching is effective in leveraging the accumulated storage size in
wireless networks by distributing different coded segments of each file in
multiple cache nodes. This paper aims to find a wireless coded caching policy
to minimize the total discounted network cost, which involves both transmission
delay and cache replacement cost, using tools from deep learning. The problem
is known to be challenging due to the unknown, time-variant content popularity
as well as the continuous, high-dimensional action space. We first propose a
clustering based long short-term memory (C-LTSM) approach to predict the number
of content requests using historical request information. This approach
exploits the correlation of the historical request information between
different files through clustering. Based on the predicted results, we then
propose a supervised deep deterministic policy gradient (SDDPG) approach. This
approach, on one hand, can learn the caching policy in continuous action space
by using the actor-critic architecture. On the other hand, it accelerates the
learning process by pre-training the actor network based on the solution of an
approximate problem that minimizes the per-slot cost. Real-world trace-based
numerical results show that the proposed prediction and caching policy using
deep learning outperform the considered existing methods.
