Please use this identifier to cite or link to this item: http://localhost:8081/jspui/handle/123456789/21619
Title: Investigation of orthogonal functions for digital predistortion
Authors: Sainath, Sabavath
Issue Date: Jun-2023
Publisher: IIT Roorkee
Abstract: Power amplifiers play a vital role in modern communication systems, where achiev ing a balance between linearity and efficiency is crucial. However, operating amplifiers in non-linear regions to achieve high efficiency can compromise their linearity. To over come this challenge, digital predistortion (DPD) techniques have emerged as effective solutions, offering improved linearity while maintaining high efficiency. Digital predistortion (DPD) is a widely adopted technique for enhancing the linear ity of power amplifiers. It is valued for its efficient resource utilization and straight forward algorithm. While there are several approaches to implementing DPD, methods based on Volterra series have gained significant attention due to their versatility and ease of implementation. When selecting a Volterra series-based method for digital predistortion (DPD), the choice of a specific method can be challenging due to the numerous options available. In this study, three distinct Volterra-based methods for DPD are investigated, and their performance is evaluated using LTE signals with two power amplifiers. Furthermore, the study examines the forward behavioral modeling performance of these three meth ods for each amplifier, employing the same set of signals. The work is organized into four chapters. Chapter 1 serves as an Introduction and provides a theoretical overview of power amplifier characteristics. In Chapter 2, The Polynomial Basis Digital Predistorter for power amplifiers is discussed. Chapter 3 delves into the detailed examination of signal characteristics and model stability, along with their associated parameters. Finally, Chapter 4 focuses on the Implementation of Digital Predistortion.
URI: http://localhost:8081/jspui/handle/123456789/21619
Research Supervisor/ Guide: Rawat, Meenakshi
metadata.dc.type: Dissertations
Appears in Collections:MASTERS' THESES (E & C)

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