Multiobjective Reinforcement Learning Based Energy Consumption in C-RAN enabled Massive MIMO

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dc.contributor.authorSharma, Shruti
dc.contributor.authorYoon, Wonsik
dc.coverage.issue1cs
dc.coverage.volume31cs
dc.date.accessioned2022-04-29T07:44:22Z
dc.date.available2022-04-29T07:44:22Z
dc.date.issued2022-04cs
dc.description.abstractMultiobjective optimization has become a suitable method to resolve conflicting objectives and enhance the performance evaluation of wireless networks. In this study, we consider a multiobjective reinforcement learning (MORL) approach for the resource allocation and energy consumption in C-RANs. We propose the MORL method with two conflicting objectives. Herein, we define the state and action spaces, and reward for the MORL agent. Furthermore, we develop a Q-learning algorithm that controls the ON-OFF action of remote radio heads (RRHs) depending on the position and nearby users with goal of selecting the best single policy that optimizes the trade-off between EE and QoS. We analyze the performance of our Q-learning algorithm by comparing it with simple ON-OFF scheme and heuristic algorithm. The simulation results demonstrated that normalized ECs of simple ON-OFF, heuristic and Q-learning algorithm were 0.99, 0.85, and 0.8 respectively. Our proposed MORL-based Q-learning algorithm achieves superior EE performance compared with simple ON-OFF scheme and heuristic algorithms.en
dc.formattextcs
dc.format.extent155-163cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2022 vol. 31, č. 1, s. 155-163. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2022.0155en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/204143
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2022/22_01_0155_0163.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectConvergenceen
dc.subjectenergy consumptionen
dc.subjectreinforcement learningen
dc.subjectrewarden
dc.subjectoptimizationen
dc.titleMultiobjective Reinforcement Learning Based Energy Consumption in C-RAN enabled Massive MIMOen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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