Decentralized Group Sparse Beamforming for Multi-Cloud Radio Access Networks

Oussama Najeeb Dhifallah, Hayssam Dahrouj, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Recent studies on cloud-radio access networks (CRANs) assume the availability of a single processor (cloud) capable of managing the entire network performance; inter-cloud interference is treated as background noise. This paper considers the more practical scenario of the downlink of a CRAN formed by multiple clouds, where each cloud is connected to a cluster of multiple-antenna base stations (BSs) via high-capacity wireline backhaul links. The network is composed of several disjoint BSs' clusters, each serving a pre-known set of single-antenna users. To account for both inter- cloud and intra-cloud interference, the paper considers the problem of minimizing the total network power consumption subject to quality of service constraints, by jointly determining the set of active BSs connected to each cloud and the beamforming vectors of every user across the network. The paper solves the problem using Lagrangian duality theory through a dual decomposition approach, which decouples the problem into multiple and independent subproblems, the solution of which depends on the dual optimization problem. The solution then proceeds in updating the dual variables and the active set of BSs at each cloud iteratively. The proposed approach leads to a distributed implementation across the multiple clouds through a reasonable exchange of information between adjacent clouds. The paper further proposes a centralized solution to the problem. Simulation results suggest that the proposed algorithms significantly outperform the conventional per-cloud update solution, especially at high signal-to-interference-plus- noise ratio (SINR) target.
    Original languageEnglish (US)
    Title of host publication2015 IEEE Global Communications Conference (GLOBECOM)
    ISBN (Print)9781479959525
    StatePublished - Mar 28 2016

    Bibliographical note

    KAUST Repository Item: Exported on 2020-10-01
    Acknowledged KAUST grant number(s): EE002355
    Acknowledgements: Hayssam Dahrouj would like to thank Effat University in Jeddah, Saudi Arabia, for funding the research reported in this paper through the Research and Consultancy Institute.
    The work of M.-S. Alouini was supported by the Qatar National Research Fund (a member of Qatar Foundation) under NPRP Grant NPRP 5-250-2-087. The statements made herein are solely the responsibility of the authors.
    The authors would like to acknowledge the support provided by King Fahd University of Petroleum and Minerals (KFUPM) and King Abdullah University of Science and Technology (KAUST) for funding this work through
    the Research Institute project number EE002355.


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