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dc.contributor.authorAlQerm, Ismail
dc.contributor.authorShihada, Basem
dc.date.accessioned2019-07-03T12:16:35Z
dc.date.available2019-07-03T12:16:35Z
dc.date.issued2019
dc.identifier.citationAlQerm, I., & Shihada, B. (2019). Energy Efficient Traffic Offloading in Multi-Tier Heterogeneous 5G Networks Using Intuitive Online Reinforcement Learning. IEEE Transactions on Green Communications and Networking, 3(3), 691–702. doi:10.1109/tgcn.2019.2916900
dc.identifier.doi10.1109/TGCN.2019.2916900
dc.identifier.urihttp://hdl.handle.net/10754/655897
dc.description.abstractThe energy efficiency in multi-tier heterogeneous 5G networks is a critical issue due to the fact that the macro base stations (BSs) power consumption is considerably high and proportional to their traffic load. Traffic offloading from macrocells to small cells is envisioned as a potential solution to improve energy efficiency in 5G heterogeneous networks. However, traffic offloading causes traffic congestion at small cells, interference as a result of small cells transmissions, and aggregate power consumption of small cells. These factors make the traffic offloading procedure more challenging. In this paper, we propose a conditional traffic offloading scheme, which relies on macrocells and small cells system load information to determine the most energy efficient traffic offloading strategy, select the proper operation mode of small cells, and fulfill macro users applications’ quality of service (QoS) requirements. The proposed scheme is developed using a novel intuitive online reinforcement learning methodology to perform the conditional traffic offloading in which each macro BS conjectures the offloading strategies of other macrocells. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant energy efficiency.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8713902/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8713902
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subject5G heterogeneous networks
dc.subjectTraffic offloading
dc.subjectOnline reinforcement learning
dc.subjectEnergy efficiency
dc.subjectmacrocells
dc.subjectsmall cells.
dc.titleEnergy Efficient Traffic Offloading in Multi-tier Heterogeneous 5G Networks Using Intuitive Online Reinforcement Learning
dc.typeArticle
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Green Communications and Networking
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mathematics and Computer Science at University of Missouri Saint Louis, St. Louis MO, United States.
kaust.personShihada, Basem
refterms.dateFOA2019-07-03T12:17:20Z


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