Publication:
Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm

Simple item page
dc.citedby24
dc.contributor.authorDarzi S.en_US
dc.contributor.authorSieh Kiong T.en_US
dc.contributor.authorTariqul Islam M.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorKibria S.en_US
dc.contributor.authorSalem B.en_US
dc.contributor.authorid55651612500en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid55328836300en_US
dc.contributor.authorid7401908770en_US
dc.contributor.authorid55637259500en_US
dc.contributor.authorid57769851500en_US
dc.date.accessioned2023-05-16T02:46:26Z
dc.date.available2023-05-16T02:46:26Z
dc.date.issued2014
dc.description.abstractLinear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. © 2014 Soodabeh Darzi et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo724639
dc.identifier.doi10.1155/2014/724639
dc.identifier.scopus2-s2.0-84934889577
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84934889577&doi=10.1155%2f2014%2f724639&partnerID=40&md5=d519cc9819b446894810210094e45f07
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21981
dc.identifier.volume2014
dc.publisherHindawi Publishing Corporationen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific World Journal
dc.titleNull steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS