Persistent Link:
http://hdl.handle.net/10150/195857
Title:
Genetic Algorithms for Optimization of Wireless Devices
Author:
Gaviria Gomez, Natalia
Issue Date:
2006
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
As a result of the evolution in communications technology, the number of wireless nodes is growing much more quickly than the number of wired nodes. Although they offer significant benefit, wireless communications systems pose challenging design problems due to the unpredictable nature of the channel. In spite of these challenges, end users are demanding the benefits of wireless connectivity with quality similar to that of wired connectivity, which requires the devices to be able to adapt to new situations in order to mitigate the detrimental effects of the wireless channel. This dissertation presents a three-layer self-awareness mechanism embedded in the management and control plane architecture of the devices used in a wireless communications network. The proposed mechanism consists of Monitoring, Situational-awareness and Adaptation functions.The dissertation focuses on the implementation of the Adaptation function, which is at the core of the self-awareness mechanism. Using an adaptive genetic algorithm (GA) as the underlying technology, the device is able to learn and apply the acquired knowledge to adapt its operational parameters when facing unknown situations. The self-adaptive capability embedded in the GA allows the evolution parameters to be adjusted according to the current point in the search. The 'knowledge' gained by each species is embodied in the makeup of the genetic information through the chromosomes.The genetic algorithm was tested in a multi-objective scenario that aims to find the weight distribution of a linear antenna array. The goal was to obtain patterns with low sidelobe levels, while maximizing the efficiency of the array. This is achieved by setting a desired effective radiation voltage (ERV). The results of the algorithm, which were compared to those obtained using simulated annealing, show that GA exhibits a more robust performance for different values of ERV. The capability of the algorithm to deal with possible sources of interference was also tested. The patterns obtained in this case showed attenuations of around 60 dB in the direction of the jammers, while maintaining the desired ERV value. While previous approaches to this problem have dealt with different issues, this is the first reported system that optimizes these parameters simultaneously.
Type:
text; Electronic Dissertation
Degree Name:
DEng
Degree Level:
doctoral
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Rodriguez, Jeffrey J.; McNeill, Kevin M.
Committee Chair:
Rodriguez, Jeffrey J.; McNeill, Kevin M.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleGenetic Algorithms for Optimization of Wireless Devicesen_US
dc.creatorGaviria Gomez, Nataliaen_US
dc.contributor.authorGaviria Gomez, Nataliaen_US
dc.date.issued2006en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAs a result of the evolution in communications technology, the number of wireless nodes is growing much more quickly than the number of wired nodes. Although they offer significant benefit, wireless communications systems pose challenging design problems due to the unpredictable nature of the channel. In spite of these challenges, end users are demanding the benefits of wireless connectivity with quality similar to that of wired connectivity, which requires the devices to be able to adapt to new situations in order to mitigate the detrimental effects of the wireless channel. This dissertation presents a three-layer self-awareness mechanism embedded in the management and control plane architecture of the devices used in a wireless communications network. The proposed mechanism consists of Monitoring, Situational-awareness and Adaptation functions.The dissertation focuses on the implementation of the Adaptation function, which is at the core of the self-awareness mechanism. Using an adaptive genetic algorithm (GA) as the underlying technology, the device is able to learn and apply the acquired knowledge to adapt its operational parameters when facing unknown situations. The self-adaptive capability embedded in the GA allows the evolution parameters to be adjusted according to the current point in the search. The 'knowledge' gained by each species is embodied in the makeup of the genetic information through the chromosomes.The genetic algorithm was tested in a multi-objective scenario that aims to find the weight distribution of a linear antenna array. The goal was to obtain patterns with low sidelobe levels, while maximizing the efficiency of the array. This is achieved by setting a desired effective radiation voltage (ERV). The results of the algorithm, which were compared to those obtained using simulated annealing, show that GA exhibits a more robust performance for different values of ERV. The capability of the algorithm to deal with possible sources of interference was also tested. The patterns obtained in this case showed attenuations of around 60 dB in the direction of the jammers, while maintaining the desired ERV value. While previous approaches to this problem have dealt with different issues, this is the first reported system that optimizes these parameters simultaneously.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
thesis.degree.nameDEngen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorRodriguez, Jeffrey J.en_US
dc.contributor.advisorMcNeill, Kevin M.en_US
dc.contributor.chairRodriguez, Jeffrey J.en_US
dc.contributor.chairMcNeill, Kevin M.en_US
dc.contributor.committeememberRodriguez, Jeffrey J.en_US
dc.contributor.committeememberMcNeill, Kevin M.en_US
dc.contributor.committeememberMcIde, Kathleen L.en_US
dc.identifier.proquest1510en_US
dc.identifier.oclc659747463en_US
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