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A compound of several clouds, jointly managing large-scale inter-cloud and intra-cloud interference, promises to be a practical solution to account for the ambitious premises of beyond fifth generation networks. This paper considers a multi-cloud radio access network (MC-RAN), where each cloud is connected to a distinct set of cache-enabled 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 subject to fronthaul capacity and transmit power constraints. This paper solves such a mixed discrete-continuous, non-convex optimization problem using fractional programming and successive inner-convex approximation 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 multiple clouds through a reasonable amount of information exchange. The numerical simulations illustrate the pronounced role the proposed algorithm plays in improving the energy efficiency of large-scale cache-enabled MC-RANs, especially at the high interference regime.

Distributed Resource Management in Downlink Cache-Enabled Multi-Cloud Radio Access Networks