Price-based joint beamforming and spectrum management in multi-antenna cognitive radio networks

Nguyen, DN; Krunz, M

Price-based joint beamforming and spectrum management in multi-antenna cognitive radio networks

Nguyen, DN

Krunz, M

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Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Areas in Communications, 2012, 30 (11), pp. 2295 - 2305
Issue Date:: 2012-12-04

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Field	Value	Language
dc.contributor.author	Nguyen, DN https://orcid.org/0000-0003-2659-8648	en_US
dc.contributor.author	Krunz, M	en_US
dc.date.issued	2012-12-04	en_US
dc.identifier.citation	IEEE Journal on Selected Areas in Communications, 2012, 30 (11), pp. 2295 - 2305	en_US
dc.identifier.issn	0733-8716	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114844
dc.description.abstract	We consider the problem of maximizing the throughput of a multi-antenna cognitive radio (CR) network. With spatial multiplexing over each frequency band, a multi-antenna CR node controls its antenna radiation directions and allocates power for each data stream by appropriately adjusting its precoding matrix. Our objective is to design a set of precoding matrices (one per band) at each CR node so that power and spectrum are optimally allocated for the node and its interference is steered away from unintended receivers. The problem is non-convex, with the number of variables growing quadratically with the number of antenna elements. To tackle it, we translate it into a noncooperative game. We derive an optimal pricing policy for each node, which adapts to the node's neighboring conditions and drives the game to a Nash-Equilibrium (NE). The network throughput under this NE equals to that of a locally optimal solution of the non-convex centralized problem. To find the set of precoding matrices at each node (best response), we develop a low-complexity distributed algorithm by exploiting the strong duality of the convex per-user optimization problem. The number of variables in the distributed algorithm is independent of the number of antenna elements. A centralized (cooperative) algorithm is also developed. Simulations show that the network throughput under the distributed algorithm rapidly converges to that of the centralized one. Finally, we develop a MAC protocol that implements our resource allocation and beamforming scheme. Extensive simulations show that the proposed protocol dramatically improves the network throughput and reduces power consumption. © 1983-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Journal on Selected Areas in Communications	en_US
dc.relation.isbasedon	10.1109/JSAC.2012.121221	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Price-based joint beamforming and spectrum management in multi-antenna cognitive radio networks	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	30	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

We consider the problem of maximizing the throughput of a multi-antenna cognitive radio (CR) network. With spatial multiplexing over each frequency band, a multi-antenna CR node controls its antenna radiation directions and allocates power for each data stream by appropriately adjusting its precoding matrix. Our objective is to design a set of precoding matrices (one per band) at each CR node so that power and spectrum are optimally allocated for the node and its interference is steered away from unintended receivers. The problem is non-convex, with the number of variables growing quadratically with the number of antenna elements. To tackle it, we translate it into a noncooperative game. We derive an optimal pricing policy for each node, which adapts to the node's neighboring conditions and drives the game to a Nash-Equilibrium (NE). The network throughput under this NE equals to that of a locally optimal solution of the non-convex centralized problem. To find the set of precoding matrices at each node (best response), we develop a low-complexity distributed algorithm by exploiting the strong duality of the convex per-user optimization problem. The number of variables in the distributed algorithm is independent of the number of antenna elements. A centralized (cooperative) algorithm is also developed. Simulations show that the network throughput under the distributed algorithm rapidly converges to that of the centralized one. Finally, we develop a MAC protocol that implements our resource allocation and beamforming scheme. Extensive simulations show that the proposed protocol dramatically improves the network throughput and reduces power consumption. © 1983-2012 IEEE.

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http://hdl.handle.net/10453/114844