Opportunistic channel access and RF energy harvesting in cognitive radio networks

Hoang, DT; Niyato, D; Wang, P; Kim, DI

Opportunistic channel access and RF energy harvesting in cognitive radio networks

Hoang, DT Niyato, D Wang, P Kim, DI

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Areas in Communications, 2014, 32, (11), pp. 2039-2052
Issue Date:: 2014-11-01

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Field	Value	Language
dc.contributor.author	Hoang, DT
dc.contributor.author	Niyato, D
dc.contributor.author	Wang, P
dc.contributor.author	Kim, DI
dc.date.accessioned	2023-03-21T05:32:30Z
dc.date.available	2023-03-21T05:32:30Z
dc.date.issued	2014-11-01
dc.identifier.citation	IEEE Journal on Selected Areas in Communications, 2014, 32, (11), pp. 2039-2052
dc.identifier.issn	0733-8716
dc.identifier.issn	1558-0008
dc.identifier.uri	http://hdl.handle.net/10453/167966
dc.description.abstract	Radio frequency (RF) energy harvesting is a promising technique to sustain operations of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. In this paper, we consider such a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively. We present an optimization formulation to obtain the channel access policy for the secondary user to maximize its throughput. Both the case that the secondary user knows the current state of the channels and the case that the secondary knows the idle channel probabilities of channels in advance are considered. However, the optimization requires model parameters (e.g., the probability of successful packet transmission, the probability of successful RF energy harvesting, and the probability of channel to be idle) to obtain the policy. To obviate such a requirement, we apply an online learning algorithm that can observe the environment and adapt the channel access action accordingly without any a prior knowledge about the model parameters. We evaluate both the efficiency and convergence of the learning algorithm. The numerical results show that the policy obtained from the learning algorithm can achieve the performance in terms of throughput close to that obtained from the optimization.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Journal on Selected Areas in Communications
dc.relation.isbasedon	10.1109/JSAC.2014.141108
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Opportunistic channel access and RF energy harvesting in cognitive radio networks
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-21T05:32:28Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	11

Abstract:

Radio frequency (RF) energy harvesting is a promising technique to sustain operations of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. In this paper, we consider such a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively. We present an optimization formulation to obtain the channel access policy for the secondary user to maximize its throughput. Both the case that the secondary user knows the current state of the channels and the case that the secondary knows the idle channel probabilities of channels in advance are considered. However, the optimization requires model parameters (e.g., the probability of successful packet transmission, the probability of successful RF energy harvesting, and the probability of channel to be idle) to obtain the policy. To obviate such a requirement, we apply an online learning algorithm that can observe the environment and adapt the channel access action accordingly without any a prior knowledge about the model parameters. We evaluate both the efficiency and convergence of the learning algorithm. The numerical results show that the policy obtained from the learning algorithm can achieve the performance in terms of throughput close to that obtained from the optimization.

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http://hdl.handle.net/10453/167966