Distributed Resource Management in Downlink Cache-enabled Multi-cloud Radio Access Networks

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.