Nonsmooth optimization for beamforming in cognitive multicast transmission

A. H. Phan, H. D. Tuan, H. H. Kha, D. T. Ngo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2010 IEEE Global Telecommunications Conference, GLOBECOM 2010
Publication statusPublished - 2010
Externally publishedYes
Event53rd IEEE Global Communications Conference, GLOBECOM 2010 - Miami, FL, United States
Duration: 6 Dec 201010 Dec 2010

Publication series

NameGLOBECOM - IEEE Global Telecommunications Conference


Conference53rd IEEE Global Communications Conference, GLOBECOM 2010
Country/TerritoryUnited States
CityMiami, FL


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