Conventional cloud-radio access networks assume the existence of a single processor, responsible for managing a plurality of devices. To cope with the current drastic increase in the number of data-hungry systems, several clouds would be practically needed, and so the joint provisioning of inter-cloud and intra-cloud interference becomes a fundamental challenge. 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 paper investigates the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide sum-rate utility. The paper solves such a difficult non-convex combinatorial problem using a heuristic algorithm which uses fractional programming techniques to deal with the non-convexity of the continuous part of the problem, and 10-norm approximation to account for the binary association part. A highlight of the proposed algorithm is its ability to be implemented in a distributed fashion across the multiple clouds. The simulations illustrate how close is the sum-rate performance of the proposed approach as compared to the sum-rate achieved by a centralized single processor, especially in dense networks.
|Original language||English (US)|
|Title of host publication||2020 IEEE International Conference on Communications Workshops (ICC Workshops)|
|State||Published - 2020|