Power minimization in MIMO cognitive networks using beamforming games

Nguyen, DN; Krunz, M

Power minimization in MIMO cognitive networks using beamforming games

Nguyen, DN

Krunz, M

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Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Areas in Communications, 2013, 31 (5), pp. 916 - 925
Issue Date:: 2013-05-01

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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	2013-05-01	en_US
dc.identifier.citation	IEEE Journal on Selected Areas in Communications, 2013, 31 (5), pp. 916 - 925	en_US
dc.identifier.issn	0733-8716	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116700
dc.description.abstract	We consider a multi-channel multi-user cognitive radio MIMO network in which each node controls its antenna radiation directions and allocates power for each data stream by adjusting its precoding matrices. Under a noncooperative game, we optimize the set of precoding matrices (one per channel) at each node so as to minimize the total transmit power in the network. Using recession analysis and the theory of variational inequalities, we obtain sufficient conditions that guarantee the existence and uniqueness of the game's Nash Equilibrium (NE). Low-complexity distributed algorithms are also developed by exploiting the strong duality of the convex per-user optimization problem. To improve the efficiency of the NE, we introduce pricing policies that employ a novel network interference function. Existence and uniqueness of the new NE under pricing are studied. Simulations confirm the effectiveness of our joint optimization approach. © 1983-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Journal on Selected Areas in Communications	en_US
dc.relation.isbasedon	10.1109/JSAC.2013.130510	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Power minimization in MIMO cognitive networks using beamforming games	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	31	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	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	31	en_US

Abstract:

We consider a multi-channel multi-user cognitive radio MIMO network in which each node controls its antenna radiation directions and allocates power for each data stream by adjusting its precoding matrices. Under a noncooperative game, we optimize the set of precoding matrices (one per channel) at each node so as to minimize the total transmit power in the network. Using recession analysis and the theory of variational inequalities, we obtain sufficient conditions that guarantee the existence and uniqueness of the game's Nash Equilibrium (NE). Low-complexity distributed algorithms are also developed by exploiting the strong duality of the convex per-user optimization problem. To improve the efficiency of the NE, we introduce pricing policies that employ a novel network interference function. Existence and uniqueness of the new NE under pricing are studied. Simulations confirm the effectiveness of our joint optimization approach. © 1983-2012 IEEE.

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http://hdl.handle.net/10453/116700