Behavioural Modeling and Linearization of RF Power Amplifier Using Artificial Neural Networks

Download or read book Behavioural Modeling and Linearization of RF Power Amplifier Using Artificial Neural Networks written by Farouk Mkadem and published by . This book was released on 2010 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.