Behavioural Modeling And Linearization Of Rf Power Amplifier Using Artificial Neural Networks
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Behavioral Modeling and Linearization of RF Power Amplifiers
Wireless voice and data communications have made great improvements, with connectivity now virtually ubiquitous. Users are demanding essentially perfect transmission and reception of voice and data. The infrastructure that supports this wide connectivity and nearly error-free delivery of information is complex, costly, and continually being improved. This resource describes the mathematical methods and practical implementations of linearization techniques for RF power amplifiers for mobile communications. This includes a review of RF power amplifier design for high efficiency operation. Readers are also provided with mathematical approaches to modeling nonlinear dynamical systems, which can be applied in the context of modeling the PA for identification in a pre-distortion system. This book also describes typical approaches to linearization and digital pre-distortion that are used in practice.
Behavioural Modeling and Linearization of RF Power Amplifier Using Artificial Neural Networks
Power Amplifiers (PAs) are the key building blocks of the emerging wireless radios systems. They dominate the power consumption and sources of distortion, especially when driven with modulated signals. Several approaches have been devised to characterize the nonlinearity of a PA. Among these approaches, dynamic amplitude (AM/AM) and phase (AM/PM) distortion characteristics are widely used to characterize the PA nonlinearity and its effects on the output signal in power, frequency or time domains, when driven with realistic modulated signals. The inherent nonlinear behaviour of PAs generally yield output signals with an unacceptable quality, an undesirable level of out-of-band emission, high Error Vector Magnitudes (EVMs) and low Adjacent Channel Power Ratios (ACPRs), which usually fail to meet the established performance standards. Traditionally, PAs are forced to operate deeply in their back-off region, far from their power capacity, in order to pass the mandatory spectrum mask (ACPR requirement) and to achieve acceptable EVM. Despite its simplicity, this solution is increasingly discarded, as it leads to cost and power inefficient radios. Alternatively, several linearization techniques, such as feedback, feed-forward and predistortion, have been devised to tackle PA nonlinearity and, consequently, improve the achievable the linearity versus power efficiency trade-off. Among these linearization techniques, the Digital Pre-Distortion (DPD) technique consists of incorporating an extra nonlinear function before the PA, in order to preprocess the input signal to the PA, so that the overall cascaded systems behave linearly. The overall linearity of the cascaded system (DPD plus PA) relies primarily on the ability of the DPD function to produce nonlinearities that are equal in magnitude and out-of-phase to those generated by the PA. Hence, a good understanding and accurate modeling of PA distortions is a crucial step in the construction of an adequate DPD function. This thesis explores DPD through techniques based on Artificial Neural Networks (ANNs). The choice of ANN as a modeling tool was motivated by its proven strength in modeling dynamic nonlinear systems.
Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers
This book is a summary of a series of achievements made by the authors and colleagues in the areas of radio frequency power amplifier modeling (including neural Volterra series modeling, neural network modeling, X-parameter modeling), nonlinear analysis methods, and power amplifier predistortion technology over the past 10 years. The book is organized into ten chapters, which respectively describe an overview of research of power amplifier behavioral models and predistortion technology, nonlinear characteristics of power amplifiers, power amplifier behavioral models and the basis of nonlinear analysis, an overview of power amplifier predistortion, Volterra series modeling of power amplifiers, power amplifier modeling based on neural networks, power amplifier modeling with X-parameters, the modeling of other power amplifiers, nonlinear circuit analysis methods, and predistortion algorithms and applications. Blending theory with analysis, this book will provide researchers and RF/microwave engineering students with a valuable resource.