Show simple item record

dc.contributor.authorComşa, Ioan-Sorin
dc.contributor.authorZhang, Sijing
dc.contributor.authorAydin, Mehmet Emin
dc.contributor.authorChen, Jianping
dc.contributor.authorKuonen, Pierre
dc.contributor.authorWagen, Jean–Frédéric
dc.date.accessioned2020-08-06T10:51:05Z
dc.date.available2020-08-06T10:51:05Z
dc.date.issued2015-02-12
dc.identifier.citationComşa I, Zhang S, Aydin M, Chen J, Kuonen P, Wagen J (2014) 'Adaptive proportional fair parameterization based LTE scheduling using continuous actor-critic reinforcement learning', 2014 IEEE Global Communications Conference - Austin, Institute of Electrical and Electronics Engineers Inc..en_US
dc.identifier.isbn9781479935116
dc.identifier.doi10.1109/GLOCOM.2014.7037498
dc.identifier.urihttp://hdl.handle.net/10547/624328
dc.description.abstractMaintaining a desired trade-off performance between system throughput maximization and user fairness satisfaction constitutes a problem that is still far from being solved. In LTE systems, different tradeoff levels can be obtained by using a proper parameterization of the Generalized Proportional Fair (GPF) scheduling rule. Our approach is able to find the best parameterization policy that maximizes the system throughput under different fairness constraints imposed by the scheduler state. The proposed method adapts and refines the policy at each Transmission Time Interval (TTI) by using the Multi-Layer Perceptron Neural Network (MLPNN) as a non-linear function approximation between the continuous scheduler state and the optimal GPF parameter(s). The MLPNN function generalization is trained based on Continuous Actor-Critic Learning Automata Reinforcement Learning (CACLA RL). The double GPF parameterization optimization problem is addressed by using CACLA RL with two continuous actions (CACLA-2). Five reinforcement learning algorithms as simple parameterization techniques are compared against the novel technology. Simulation results indicate that CACLA-2 performs much better than any of other candidates that adjust only one scheduling parameter such as CACLA-1. CACLA-2 outperforms CACLA-1 by reducing the percentage of TTIs when the system is considered unfair. Being able to attenuate the fluctuations of the obtained policy, CACLA-2 achieves enhanced throughput gain when severe changes in the scheduling environment occur, maintaining in the same time the fairness optimality condition.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.urlhttps://ieeexplore.ieee.org/abstract/document/7037498en_US
dc.subjectCACLA-2en_US
dc.subjectMLPNNen_US
dc.subjectscheduling ruleen_US
dc.subjectpolicyen_US
dc.subjectTTIen_US
dc.subjectGPFen_US
dc.subjectCQIen_US
dc.subjectfairnessen_US
dc.subjectCACLA-1en_US
dc.subjectLTE-Aen_US
dc.subjectthroughputen_US
dc.titleAdaptive proportional fair parameterization based LTE scheduling using continuous actor-critic reinforcement learningen_US
dc.typeConference papers, meetings and proceedingsen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.contributor.departmentUniversity of Applied Sciences of Western Switzerlanden_US
dc.date.updated2020-08-06T10:37:29Z
dc.description.note


This item appears in the following Collection(s)

Show simple item record