  In light of the premises of beyond fifth generation (B5G) networks, the need
for better exploiting the capabilities of cloud-enabled networks arises, so as
to cope with the large-scale interference resulting from the massive increase
of data-hungry systems. A compound of several clouds, jointly managing
inter-cloud and intra-cloud interference, constitutes a practical solution to
account for the requirements of B5G networks. This paper considers a
multi-cloud radio access network model (MC-RAN), where each cloud is connected
to a distinct set of base stations (BSs) via limited capacity fronthaul links.
The BSs are equipped with local cache storage and baseband processing
capabilities, as a means to alleviate the fronthaul congestion problem. The
paper then investigates the problem of jointly assigning users to clouds and
determining their beamforming vectors so as to maximize the network-wide energy
efficiency (EE) subject to fronthaul capacity and transmit power constraints.
This paper solves such a mixed discrete-continuous, non-convex optimization
problem using fractional programming (FP) and successive inner-convex
approximation (SICA) techniques to deal with the non-convexity of the
continuous part of the problem, and $l_0$-norm approximation to account for the
binary association part. A highlight of the proposed algorithm is its
capability of being implemented in a distributed fashion across the network's
multiple clouds through a reasonable amount of information exchange. The
numerical simulations illustrate the pronounced role the proposed algorithm
plays in alleviating the interference of large-scale MC-RANs, especially in
dense networks.
