  Popular content caching is expected to play a major role in efficiently
reducing backhaul congestion and achieving user satisfaction in next generation
mobile radio systems. Consider the downlink of a cache-enabled cloud radio
access network (CRAN), where each cache-enabled base station (BS) is equipped
with limited-size local cache storage. The central computing unit (cloud) is
connected to the BSs via a limited capacity backhaul link and serves a set of
single-antenna mobile users (MUs). This paper assumes that only imperfect
channel state information (CSI) is available at the cloud. It focuses on the
problem of minimizing the total network power and backhaul cost so as to
determine the beamforming vector of each user across the network, the
quantization noise covariance matrix, and the BS clustering subject to
imperfect channel state information and fixed cache placement assumptions. The
paper suggests solving such a difficult, non-convex optimization problem using
the semidefinite relaxation (SDR). The paper then uses the $\ell_0$-norm
approximation to provide a feasible, sub-optimal solution using the
majorization-minimization (MM) approach. Simulation results particularly show
how the cache-enabled network significantly improves the backhaul cost
especially at high signal-to-interference-plus-noise ratio (SINR) values as
compared to conventional cache-less CRANs.
