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 l0-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.
Bibliographical noteKAUST Repository Item: Exported on 2022-09-14
Acknowledged KAUST grant number(s): ORA-CRG2021-4695
Acknowledgements: The work of R.-J. Reifert and A. Sezgin was supported by the German Federal Ministry of Education and Research (BMBF) in the course of the 6GEM Research Hub under Grant 16KISK037. The work of H. Dahrouj was supported by the Center of Excellence for NEOM Research at the King Abdullah University of Science and Technology (KAUST). The work of T. Y. Al-Naffouri was supported by the KAUST Office of Sponsored Research (OSR) under Award No. ORA-CRG2021-4695.
ASJC Scopus subject areas
- Automotive Engineering
- Applied Mathematics
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Aerospace Engineering