  While next-generation wireless communication networks intend leveraging edge
caching for enhanced spectral efficiency, quality of service, end-to-end
latency, content sharing cost, etc., several aspects of it are yet to be
addressed to make it a reality. One of the fundamental mysteries in a
cache-enabled network is predicting what content to cache and where to cache so
that high caching content availability is accomplished. For simplicity, most of
the legacy systems utilize a static estimation - based on Zipf distribution,
which, in reality, may not be adequate to capture the dynamic behaviors of the
contents popularities. Forecasting user's preferences can proactively allocate
caching resources and cache the needed contents, which is especially important
in a dynamic environment with real-time service needs. Motivated by this, we
propose a long short-term memory (LSTM) based sequential model that is capable
of capturing the temporal dynamics of the users' preferences for the available
contents in the content library. Besides, for a more efficient edge caching
solution, different nodes in proximity can collaborate to help each other.
Based on the forecast, a non-convex optimization problem is formulated to
minimize content sharing costs among these nodes. Moreover, a greedy algorithm
is used to achieve a sub-optimal solution. By using mathematical analysis and
simulation results, we validate that the proposed algorithm performs better
than other existing schemes.
