  This paper develops a stochastic geometry-based approach for the modeling,
analysis, and optimization of wireless cloud caching networks comprised of
multiple-antenna radio units (RUs) inside clouds. We consider the Matern
cluster process to model RUs and the probabilistic content placement to cache
files in RUs. Accordingly, we study the exact hit probability for a user of
interest for two strategies; closest selection, where the user is served by the
closest RU that has its requested file, and best selection, where the serving
RU having the requested file provides the maximum instantaneous received power
at the user. As key steps for the analyses, the Laplace transform of out of
cloud interference, the desired link distance distribution in the closest
selection, and the desired link received power distribution in the best
selection are derived. Also, we approximate the derived exact hit probabilities
for both the closest and the best selections in such a way that the related
objective functions for the content caching design of the network can lead to
tractable concave optimization problems. Solving the optimization problems, we
propose algorithms to efficiently find their optimal content placements.
Finally, we investigate the impact of different parameters such as the number
of antennas and the cache memory size on the caching performance.
