Reinforcement learning based radio resource scheduling in LTE-advanced

In this paper, a novel radio resource scheduling policy for Long Term Evolution Advanced (LTE-A) radio access technology in downlink acceptance is proposed. The scheduling process works with dispatching rules which are various with different behaviors. In the literature, the scheduling disciplines a...

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Bibliographic Details
Published in2011 17th International Conference on Automation and Computin pp. 219 - 224
Main Authors Comsa, I. S., Aydin, M., Sijing Zhang, Kuonen, P., Wagen, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2011
Subjects
Dynamic scheduling
Indexes
IQ-III reinforcement learning
LTE-A
multi agent systems
policy adoption
Q-III learning
Quality of service
Resource management
Scheduling algorithm
scheduling rule
Throughput
TTI
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ISBN1467300004
9781467300001

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Summary:In this paper, a novel radio resource scheduling policy for Long Term Evolution Advanced (LTE-A) radio access technology in downlink acceptance is proposed. The scheduling process works with dispatching rules which are various with different behaviors. In the literature, the scheduling disciplines are applied for the entire transmission sessions and the scheduler performance strongly depends on the exploited discipline. Our method provides a straightforward schedule within transmission time interval (TTI) frame. Hence, a mixture of disciplines can be used for each TTI instead of the single one adopted across the whole transmission. The grand objective is to bring real improvements in terms of system throughput, system capacity and spectral efficiency (operator benefit) assuring in the same time the best user fairness and Quality of Services (QoS) capabilities (user benefit). In order to meet this objective, each rule must to be called on the best matching conditions. The policy adoption and refinement are the best way to optimize the use of mixture of rules. The Q-III reinforcement learning algorithm is proposed for the policy adoption in order to transform the scheduling experiences into a permanent nature, facilitating the decision-making on which rules will be used for each TTI. The IQ-III reinforcement learning algorithm using multi-agent environments refines the policy adoption by considering the agents' opinions in order to reduce the policy convergence time.
ISBN:1467300004
9781467300001