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dc.contributor.advisorRappaport, Theodore S., 1960-en
dc.contributor.advisorVeciana, Gustavo deen
dc.creatorChen, Jeremy Kang-penen
dc.date.accessioned2011-08-18T16:20:11Zen
dc.date.available2011-08-18T16:20:11Zen
dc.date.issued2007-05en
dc.identifier.urihttp://hdl.handle.net/2152/13184en
dc.descriptiontexten
dc.description.abstractThis dissertation is the first analytical and algorithmic work to exhibit the substantial gains that result from applying site specific knowledge to frequency allocation, transmit power control, and load balancing in wireless networks. Site specific knowledge refers to the use of knowledge of the surrounding propagation environment, building layouts, the locations of access points (APs) and clients, and the locations and electrical properties of physical objects. We assume a central network controller communicates with all APs, and has site specific knowledge which enables the controller to differentiate the sources of RF interference at every AP or user. By predicting the power from each interference source, the controller can allocate frequency channels, adjust transmit power levels, and balance loads among APs and clients in order to optimize throughput of the network. When site specific knowledge is not available, measurement-based algorithms may be used; we present three measurement-based frequency allocation algorithms that outperform the best published work by 18% for median user throughput. Then we present two site-specific knowledge-based frequency allocations that outperform the proposed measurement-based algorithms particularly for uplifting throughputs of the users who suffer low throughputs, e.g., we have gains of 3.75%, 11.8%, 10.2%, 18.2%, 33.3%, and 459% for 50, 25, 20, 15, 10, and 5 percentiles of user throughputs, respectively, over the proposed measurement-based algorithms. Furthermore, we employ transmit power control to further improve clients’ throughputs achieved by optimal site-specific knowledgebased frequency allocations; transmit power control can improve the 25, 10, 5, and 3 percentiles of users’ throughputs by up to 4.2%, 9.9%, 38%, and 110%, and save power by 20%. Finally, a load balancing algorithm is proposed as an add-on that works seamlessly with frequency allocation and transmit power control algorithms. The load-balancing algorithm can improve median user throughput by about 26%. The work in this dissertation shows that site specific knowledge is an important means for optimizing performance of wireless networks.
dc.format.mediumelectronicen
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subjectWireless communication systemsen
dc.subjectComputer networks--Workloaden
dc.subjectArtificial satellites in telecommunicationen
dc.titleFrequency allocation, transmit power control, and load balancing with site specific knowledge for optimizing wireless network performanceen
dc.description.departmentElectrical and Computer Engineeringen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


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