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dc.contributor.authorAlqerm, Ismail
dc.contributor.authorShihada, Basem
dc.date.accessioned2018-05-14T13:37:06Z
dc.date.available2018-05-14T13:37:06Z
dc.date.issued2018-02-15
dc.identifier.citationAlQerm I, Shihada B (2017) Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). Available: http://dx.doi.org/10.1109/PIMRC.2017.8292227.
dc.identifier.doi10.1109/PIMRC.2017.8292227
dc.identifier.urihttp://hdl.handle.net/10754/627848
dc.description.abstractHeterogeneous cloud radio access networks (H-CRAN) is a new trend of 5G that aims to leverage the heterogeneous and cloud radio access networks advantages. Low power remote radio heads (RRHs) are exploited to provide high data rates for users with high quality of service requirements (QoS), while high power macro base stations (BSs) are deployed for coverage maintenance and low QoS users support. However, the inter-tier interference between the macro BS and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRAN. Therefore, we propose a centralized resource allocation scheme using online learning, which guarantees interference mitigation and maximizes energy efficiency while maintaining QoS requirements for all users. To foster the performance of such scheme with a model-free learning, we consider users' priority in resource blocks (RBs) allocation and compact state representation based learning methodology to enhance the learning process. Simulation results confirm that the proposed resource allocation solution can mitigate interference, increase energy and spectral efficiencies significantly, and maintain users' QoS requirements.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8292227/
dc.subjectEnergy efficiency
dc.subjectH-CRAN
dc.subjectOnline learning
dc.subjectResource allocation
dc.titleEnhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journal2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
dc.conference.date2017-10-08 to 2017-10-13
dc.conference.name28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017
dc.conference.locationMontreal, QC, CAN
kaust.personAlqerm, Ismail
kaust.personShihada, Basem
dc.date.published-online2018-02-15
dc.date.published-print2017-10


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