  Small base stations (SBs) of fifth-generation (5G) cellular networks are
envisioned to have storage devices to locally serve requests for reusable and
popular contents by \emph{caching} them at the edge of the network, close to
the end users. The ultimate goal is to shift part of the predictable load on
the back-haul links, from on-peak to off-peak periods, contributing to a better
overall network performance and service experience. To enable the SBs with
efficient \textit{fetch-cache} decision-making schemes operating in dynamic
settings, this paper introduces simple but flexible generic time-varying
fetching and caching costs, which are then used to formulate a constrained
minimization of the aggregate cost across files and time. Since caching
decisions per time slot influence the content availability in future slots, the
novel formulation for optimal fetch-cache decisions falls into the class of
dynamic programming. Under this generic formulation, first by considering
stationary distributions for the costs and file popularities, an efficient
reinforcement learning-based solver known as value iteration algorithm can be
used to solve the emerging optimization problem. Later, it is shown that
practical limitations on cache capacity can be handled using a particular
instance of the generic dynamic pricing formulation. Under this setting, to
provide a light-weight online solver for the corresponding optimization, the
well-known reinforcement learning algorithm, $Q$-learning, is employed to find
optimal fetch-cache decisions. Numerical tests corroborating the merits of the
proposed approach wrap up the paper.
