Nonsmooth optimization for beamforming in cognitive multicast transmission

Phan, AH; Tuan, HD; Kha, HH; Ngo, DT

Nonsmooth optimization for beamforming in cognitive multicast transmission

Phan, AH Tuan, HD

Kha, HH Ngo, DT

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Publication Type:: Conference Proceeding
Citation:: GLOBECOM - IEEE Global Telecommunications Conference, 2010
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Phan, AH	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Kha, HH	en_US
dc.contributor.author	Ngo, DT	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	GLOBECOM - IEEE Global Telecommunications Conference, 2010	en_US
dc.identifier.isbn	9781424456383	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16514
dc.description.abstract	It is well-known that the optimal beamforming problems for cognitive multicast transmission are indefinite quadratic (nonconvex) optimization programs. The conventional approach is to reformulate them as convex semi-definite programs (SDPs) with additional rank-one (nonconvex and discontinuous) constraints. The rank-one constraints are then dropped for relaxed solutions, and randomization techniques are employed for solution search. In many practical cases, this approach fails to deliver satisfactory solutions, i.e., its found solutions are very far from the optimal ones. In contrast, in this paper we cast the optimal beamforming problems as SDPs with the additional reverse convex (but continuous) constraints. An efficient algorithm of nonsmooth optimization is then proposed for seeking the optimal solution. Our simulation results show that the proposed approach yields almost global optimal solutions with much less computational load than the mentioned conventional one. ©2010 IEEE.	en_US
dc.relation.ispartof	GLOBECOM - IEEE Global Telecommunications Conference	en_US
dc.relation.isbasedon	10.1109/GLOCOM.2010.5683915	en_US
dc.title	Nonsmooth optimization for beamforming in cognitive multicast transmission	en_US
dc.type	Conference Proceeding
utslib.for	0915 Interdisciplinary Engineering	en_US
dc.location.activity	Miami, USA	en_US
dc.location.activity	WOS:000239893000002
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

It is well-known that the optimal beamforming problems for cognitive multicast transmission are indefinite quadratic (nonconvex) optimization programs. The conventional approach is to reformulate them as convex semi-definite programs (SDPs) with additional rank-one (nonconvex and discontinuous) constraints. The rank-one constraints are then dropped for relaxed solutions, and randomization techniques are employed for solution search. In many practical cases, this approach fails to deliver satisfactory solutions, i.e., its found solutions are very far from the optimal ones. In contrast, in this paper we cast the optimal beamforming problems as SDPs with the additional reverse convex (but continuous) constraints. An efficient algorithm of nonsmooth optimization is then proposed for seeking the optimal solution. Our simulation results show that the proposed approach yields almost global optimal solutions with much less computational load than the mentioned conventional one. ©2010 IEEE.

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