Performance Optimization for Cooperative Multiuser Cognitive Radio Networks with RF Energy Harvesting Capability

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

Performance Optimization for Cooperative Multiuser Cognitive Radio Networks with RF Energy Harvesting Capability

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 Transactions on Wireless Communications, 2015, 14, (7), pp. 3614-3629
Issue Date:: 2015-07-01

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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	2023-03-21T05:36:35Z
dc.date.available	2023-03-21T05:36:35Z
dc.date.issued	2015-07-01
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2015, 14, (7), pp. 3614-3629
dc.identifier.issn	1536-1276
dc.identifier.issn	1558-2248
dc.identifier.uri	http://hdl.handle.net/10453/167969
dc.description.abstract	We study the performance optimization problem for a cognitive radio network with radio frequency (RF) energy harvesting capability for secondary users. In such networks, the secondary users are able to not only transmit packets on a channel licensed to a primary user when the channel is idle, but also harvest RF energy from the primary users' transmissions when the channel is busy. Specifically, we propose a system model where the secondary users are able to cooperate to maximize the overall network throughput through sensing a set of common channels. We first consider the case where the secondary users cooperate in a TDMA fashion and propose a novel solution based on a learning algorithm to find optimal channel access policies for the secondary users. Then, we examine the case where the secondary users cooperate in a decentralized manner and we formulate the cooperative decentralized optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve the cooperative decentralized stochastic optimization problem, we apply a decentralized learning algorithm based on the policy gradient and the Lagrange multiplier method to obtain optimal channel access policies. Extensive performance evaluation is conducted and it shows the efficiency and the convergence of the learning algorithms.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Wireless Communications
dc.relation.isbasedon	10.1109/TWC.2015.2408610
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	Performance Optimization for Cooperative Multiuser Cognitive Radio Networks with RF Energy Harvesting Capability
dc.type	Journal Article
utslib.citation.volume	14
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	2023-03-21T05:36:31Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	7

Abstract:

We study the performance optimization problem for a cognitive radio network with radio frequency (RF) energy harvesting capability for secondary users. In such networks, the secondary users are able to not only transmit packets on a channel licensed to a primary user when the channel is idle, but also harvest RF energy from the primary users' transmissions when the channel is busy. Specifically, we propose a system model where the secondary users are able to cooperate to maximize the overall network throughput through sensing a set of common channels. We first consider the case where the secondary users cooperate in a TDMA fashion and propose a novel solution based on a learning algorithm to find optimal channel access policies for the secondary users. Then, we examine the case where the secondary users cooperate in a decentralized manner and we formulate the cooperative decentralized optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve the cooperative decentralized stochastic optimization problem, we apply a decentralized learning algorithm based on the policy gradient and the Lagrange multiplier method to obtain optimal channel access policies. Extensive performance evaluation is conducted and it shows the efficiency and the convergence of the learning algorithms.

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http://hdl.handle.net/10453/167969