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dc.contributor.authorRen, FCen_US
dc.contributor.authorChang, CJen_US
dc.contributor.authorChen, YSen_US
dc.date.accessioned2019-04-02T06:04:42Z-
dc.date.available2019-04-02T06:04:42Z-
dc.date.issued2002-01-01en_US
dc.identifier.urihttp://dx.doi.org/10.1109/PIMRC.2002.1045263en_US
dc.identifier.urihttp://hdl.handle.net/11536/150558-
dc.description.abstractIn this paper, a Q-learning-based multi-rate transmission control scheme (Q-MRTC) for radio resource control (RRC) in WCDMA systems is proposed. The RRC problem is modelled as a semi-Markov decision process (SMDP). And we successfully apply a real-time reinforcement learning algorithm, named Q-learning, to accurately estimate the transmission cost for the multi-rate transmission control. For the cost function approximation, we apply the feature extraction method to map the original state space into a more compact set which represents the resultant interference profile. Simulation results show that the Q MRTC can achieve higher system throughput and better users' satisfaction index, by an amount of 87% and 50%, respectively, than the interference-based multi-rate transmission control scheme, while keeping the QoS requirement.en_US
dc.language.isoen_USen_US
dc.subjectmulti-rate transmissionen_US
dc.subjectradio resource managementen_US
dc.subjectQ-learningen_US
dc.subjectCDMA communication systemen_US
dc.titleA Q-learning-based multi-rate transmission control scheme for RRC in WCDMA systemsen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/PIMRC.2002.1045263en_US
dc.identifier.journal13TH IEEE INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS, VOL 1-5, PROCEEDINGS: SAILING THE WAVES OF THE WIRELESS OCEANSen_US
dc.citation.spage1422en_US
dc.citation.epage1426en_US
dc.contributor.department交大工研院聯合研發中心zh_TW
dc.contributor.departmentNCTU/ITRI Joint Research Centeren_US
dc.identifier.wosnumberWOS:000180377800284en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper