Reference : Novel Reinforcement Learning based Power Control and Subchannel Selection Mechanism f...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/53146

Title :	Novel Reinforcement Learning based Power Control and Subchannel Selection Mechanism for Grant-Free NOMA URLLC-Enabled Systems
Language :	English
Author, co-author :	Tran, Duc Dung [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Ha, Vu Nguyen [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom]
	Chatzinotas, Symeon [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Publication date :	Aug-2022
Main document title :	Proceedings of 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)
Pages :	1-5
Peer reviewed :	Yes
Audience :	International
Event name :	2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)
Event date :	from 19-06-2022 to 22-06-2022
Event organizer :	IEEE
Event place (city) :	Helsinki
Event country :	Finland
Keywords :	[en] Grant-free NOMA ; Q-Learning ; URLLC
Abstract :	[en] Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC-enabled 5G system with strict requirements on reliability and latency. However, GF-NOMAbased systems can suffer from severe interference caused by the grant-free (GF) access manner which may degrade the system performance and violate the URLLC-related requirements. To overcome this issue, the paper proposes a novel reinforcementlearning (RL)-based random access (RA) protocol based on which each device can learn from the previous decision and its corresponding performance to select the best subchannels and transmit power level for data transmission to avoid strong cross-interference. The learning-based framework is developed to maximize the system access efficiency which is defined as the ratio between the number of successful transmissions and the number of subchannels. Simulation results show that our proposed framework can improve the system access efficiency significantly in overloaded scenarios.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM - Signal Processing & Communications
Funders :	FNR and ERC
Name of the research project :	FNR-funded project CORE 5G-Sky (Grant C19/IS/13713801)
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/53146
FnR project :	FnR ; FNR13713801 > Bjorn Ottersten > 5G-Sky > Interconnecting The Sky In 5g And Beyond - A Joint Communication And Control Approach > 01/06/2020 > 31/05/2023 > 2019

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