Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/3646
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dc.date.accessioned2012-04-23T12:45:33Z-
dc.date.available2012-04-23T12:45:33Z-
dc.date.issued2012-04-23-
dc.identifier.urihttp://hdl.handle.net/10603/3646-
dc.description.abstractWith the advent of spectrally efficient wireless communication systems employing modulation schemes with varying amplitude of the communication signal, linearization techniques for nonlinear power amplifiers have gained significant interest. The availability of fast and cost effective digital processing technology makes digital predistortion attractive for power amplifier linearization since it promises high power efficiency and flexibility. Power amplifiers are most efficient in terms of delivered output power versus supplied power if driven near the saturation point. When driven near saturation, the power amplifier behaves as a nonlinear device, which introduces undesired distortions in the information signal. These nonlinear distortions degrade the system performance in terms of increased bit error rate and produce disturbance in adjacent channels. A compensation of the nonlinear distortions is therefore of significant importance, not only to keep the system performance high, but also to comply with regulatory specifications regarding the maximum allowed disturbance of adjacent channels. Nonlinear equalization at the receiver is possible but complicated due to the unknown effects of the channel. It is therefore easy to reduce the nonlinear distortions at the transmitter side. Different linearization methods exist which aim to reduce the nonlinear distortions while keeping the power amplifier in the nonlinear and efficient mode. Linearization by digital predistortion is a new method which applies digital signal processing techniques for compensating the nonlinear distortions. Digital predistortion task can be divided into three sub tasks: modeling of the power amplifier, adaptive identification of the model parameters, and development of the predistortion filter. These tasks have been addressed in this thesis. In the present study it has been observed that behavioral modeling approach is easy to model a power amplifier. Study shows that Volterra series can be most suitable to represent the actual behavior of non-linear power amplifier with memory. But as the order of memory polynomial increases, the number of Volterra terms also increases, resulting in the implementation complexity of Volterra series. The Memory Polynomial model, which is reduced form of Volterra series is most widely used by researchers but is complex to implement. The identification of coefficientsof Memory Polynomial is also a tedious task. So, in this thesis a simplified and easy to implement form of Memory Polynomial model of power amplifier has been proposed.en_US
dc.format.extent200p.en_US
dc.languageEnglishen_US
dc.rightsuniversityen_US
dc.titleTo develop a linearization technique for mitigating the RF power amplifier's non-linearity effects in a multicarrier W-cdma base stationen_US
dc.creator.researcherSappal, Amandeep Singhen_US
dc.subject.keywordLinearization Techniqueen_US
dc.subject.keywordElectronicsen_US
dc.subject.keywordError Vector Magnitudeen_US
dc.description.noteReferences p.177-200, Annexture p.1676-176en_US
dc.contributor.guidePatterah, Manjeet Singhen_US
dc.publisher.placePatialaen_US
dc.publisher.universityPunjabi Universityen_US
dc.publisher.institutionDepartment of Electronics and Communication Engineeringen_US
dc.date.registered0en_US
dc.date.completed2011en_US
dc.format.accompanyingmaterialNoneen_US
dc.type.degreePh.D.en_US
dc.source.inflibnetINFLIBNETen_US
Appears in Departments:Department of Electronics and Communication Engineering

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01_title.pdfAttached File18.94 kBAdobe PDFView/Open
02_certificate.pdf271.63 kBAdobe PDFView/Open
03_declaration.pdf244.17 kBAdobe PDFView/Open
04_acknowledgements.pdf310.86 kBAdobe PDFView/Open
05_abstract.pdf20.29 kBAdobe PDFView/Open
06_contents.pdf19.16 kBAdobe PDFView/Open
07_list of tables.pdf8.13 kBAdobe PDFView/Open
08_list of figures.pdf70.68 kBAdobe PDFView/Open
09_list of abbreviations.pdf13.36 kBAdobe PDFView/Open
10_chapter 1.pdf172.9 kBAdobe PDFView/Open
11_chapter 2.pdf769.95 kBAdobe PDFView/Open
12_chapter 3.pdf679.49 kBAdobe PDFView/Open
13_chapter 4.pdf1.45 MBAdobe PDFView/Open
14_chapter 5.pdf393.73 kBAdobe PDFView/Open
15_chapter 6.pdf23.39 kBAdobe PDFView/Open
16_annexture.pdf73.9 kBAdobe PDFView/Open
17_references.pdf82.69 kBAdobe PDFView/Open


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