Joint Beamforming and Clustering for Energy Efficient Multi-Cloud Radio Access Networks

• Published in: IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 608 - 613
• Main Authors: Reifert, Robert-Jeron, Ahmad, Alaa Alameer, Dahrouj, Hayssam, Chaaban, Anas, Sezgin, Aydin, Al-Naffouri, Tareq Y., Alouini, Mohamed-Slim
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.04.2022
• Subjects: Array signal processing; Clustering algorithms; Interference; Numerical simulation; Programming; Signal processing algorithms; System performance
• ISSN: 1558-2612
• DOI: 10.1109/WCNC51071.2022.9771701

The tremendous growth of data traffic in mobile communication networks (MCNs) and the associated exponential increase in mobile devices' numbers necessitate the use of multi-cloud radio access networks (MC-RANs) as a viable solution to cope with the requirements of next-generation MCNs (6G). In MC-RANs, each central processor (CP) manages the signal processing of its own set of base stations (BSs), and so the system performance becomes a function of the joint intra-cloud and inter-cloud interference mitigation techniques. To this end, this paper considers the problem of maximizing the network-wide energy efficiency (EE) subject to user-to-cloud association, fronthaul capacity, maximum transmit power, and achievable rate constraints, so as to determine the joint beamforming vector of each user and the user-to-cloud association strategy. The paper tackles the non-convex and mixed discrete-continuous nature of the problem formulation using fractional programming (FP) and inner-convex approximation (ICA) techniques, as well as l 0 -norm relaxation heuristics, and shows how the proposed approach can be implemented in a distributed fashion via a reasonable amount of information exchange across the CPs. The paper simulations highlight the appreciable algorithmic efficiency of the proposed approach over state-of-the-art schemes.