  In the scope of beyond fifth generation (B5G) networks and the massive
increase of data-hungry systems, the need of extending conventional
single-cloud radio access networks (C-RAN) arises. A compound of several
clouds, jointly managing inter-cloud and intra-cloud interference, constitutes
a practical solution to cope with 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 base-band
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 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.
