Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142886
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dc.contributor.authorYang, Helinen_US
dc.contributor.authorDu, Pengfeien_US
dc.contributor.authorZhong, Wen-Deen_US
dc.contributor.authorChen, Chenen_US
dc.contributor.authorAlphones, Arokiaswamien_US
dc.contributor.authorZhang, Shengen_US
dc.date.accessioned2020-07-07T03:20:17Z-
dc.date.available2020-07-07T03:20:17Z-
dc.date.issued2019-
dc.identifier.citationYang, H., Du, P., Zhong, W.-D., Chen, C., Alphones, A., & Zhang, S. (2019). Reinforcement learning-based intelligent resource allocation for integrated VLCP systems. IEEE Wireless Communications Letters, 8(4), 1204-1207. doi:10.1109/lwc.2019.2911682en_US
dc.identifier.issn2162-2337en_US
dc.identifier.urihttps://hdl.handle.net/10356/142886-
dc.description.abstractIn this letter, an intelligent resource allocation framework based on model-free reinforcement learning (RL) is first presented for multi-user integrated visible light communication and positioning (VLCP) systems, in order to maximize the sum rate of users while guaranteeing the users' minimum data rates and positioning accuracy constraints. The learning framework can learn the optimal policy under unknown environment's dynamics and the continuous-valued space, and a reward function is proposed to take into account the strict communication and positioning constraints. Moreover, a modified experience replay actor-critic (MERAC) RL approach is proposed to improve the learning efficiency and convergence speed, which efficiently collects the reliable experience and utilizes the most useful knowledge from the memory. Numerical results show that the MERAC approach can effectively learn to satisfy the strict constraints and achieve the fast convergence speed.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.language.isoenen_US
dc.relationSMA-RP6en_US
dc.relation.ispartofIEEE Wireless Communications Lettersen_US
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. The published version is available at: https://doi.org/10.1109/lwc.2019.2911682en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleReinforcement learning-based intelligent resource allocation for integrated VLCP systemsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/lwc.2019.2911682-
dc.description.versionAccepted versionen_US
dc.identifier.issue4en_US
dc.identifier.volume8en_US
dc.identifier.spage1204en_US
dc.identifier.epage1207en_US
dc.subject.keywordsVisible Light Communication and Positioningen_US
dc.subject.keywordsIntelligent Resource Allocationen_US
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item.grantfulltextopen-
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