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Nonlinear System Identification and Analysis with Applications to Power Amplifier Modeling and Power Amplifier Predistortion

2004 Electric distortion
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Release : 2004
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Book Synopsis Nonlinear System Identification and Analysis with Applications to Power Amplifier Modeling and Power Amplifier Predistortion by : Raviv Raich

Download or read book Nonlinear System Identification and Analysis with Applications to Power Amplifier Modeling and Power Amplifier Predistortion written by Raviv Raich. This book was released on 2004. Available in PDF, EPUB and Kindle. Book excerpt: Power amplifiers (PAs) are important components of communication systems and are inherently nonlinear. When a non-constant modulus signal goes through a nonlinear PA, spectral regrowth (broadening) appears in the PA output, which in turn causes adjacent channel interference (ACI). Stringent limits on the ACI are imposed by regulatory bodies, and thus the extent of the PA nonlinearity must be controlled. PA linearization is often necessary to suppress spectral regrowth, contain adjacent channel interference, and reduce bit error rate (BER). This dissertation addresses the following aspects of power amplifier research: modeling, linearization, and spectral regrowth analysis. We explore the passband and baseband PA input/output relationships and show that they manifest differently when the PA exhibits long-term, short-term, or no memory effects. The so-called quasi-memoryless case is especially clarified. Four particular nonlinear models with memory are further investigated. We provide experimental results to support our analysis. The benefits of using the orthogonal polynomials as opposed to the conventional polynomials are explored, in the context of digital baseband PA modeling and predistorter design. A closed-form expression for the orthogonal polynomial basis is derived. We demonstrate the improvement in numerical stability associated with the use of orthogonal polynomials for predistortion. Spectral analysis can help to evaluate the suitability of a given PA for amplifying certain signals or to assist in predistortion linearization algorithm design. With the orthogonal polynomials that we derived, spectral analysis of the nonlinear PA becomes a straightforward task. We carry out nonlinear spectral analysis with digitally modulated signal as input. We demonstrate an analytical approach for evaluating the power spectra of filtered QPSK and OQPSK signals after nonlinear amplification. Many communications devices are nonlinear and have a peak power or peak amplitude constraint. In addition to possibly amplifying the useful signal, the nonlinearity also generates distortions. We focus on signal-to-noise-and-distortion ratio (SNDR) optimization within the family of amplitude limited memoryless nonlinearities. We obtain a link between the capacity of amplitude-limited nonlinear channels with Gaussian noise to the SNDR.

Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers

2021-07-30 Technology & Engineering
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Release : 2021-07-30
Genre : Technology & Engineering
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Book Rating : 597/5 ( reviews)

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Book Synopsis Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers by : Jingchang Nan

Download or read book Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers written by Jingchang Nan. This book was released on 2021-07-30. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Inverse system identification with applications in predistortion

2018-12-19
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Release : 2018-12-19
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Book Rating : 710/5 ( reviews)

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Book Synopsis Inverse system identification with applications in predistortion by : Ylva Jung

Download or read book Inverse system identification with applications in predistortion written by Ylva Jung. This book was released on 2018-12-19. Available in PDF, EPUB and Kindle. Book excerpt: Models are commonly used to simulate events and processes, and can be constructed from measured data using system identification. The common way is to model the system from input to output, but in this thesis we want to obtain the inverse of the system. Power amplifiers (PAs) used in communication devices can be nonlinear, and this causes interference in adjacent transmitting channels. A prefilter, called predistorter, can be used to invert the effects of the PA, such that the combination of predistorter and PA reconstructs an amplified version of the input signal. In this thesis, the predistortion problem has been investigated for outphasing power amplifiers, where the input signal is decomposed into two branches that are amplified separately by highly efficient nonlinear amplifiers and then recombined. We have formulated a model structure describing the imperfections in an outphasing abbrPA and the matching ideal predistorter. The predistorter can be estimated from measured data in different ways. Here, the initially nonconvex optimization problem has been developed into a convex problem. The predistorters have been evaluated in measurements. The goal with the inverse models in this thesis is to use them in cascade with the systems to reconstruct the original input. It is shown that the problems of identifying a model of a preinverse and a postinverse are fundamentally different. It turns out that the true inverse is not necessarily the best one when noise is present, and that other models and structures can lead to better inversion results. To construct a predistorter (for a PA, for example), a model of the inverse is used, and different methods can be used for the estimation. One common method is to estimate a postinverse, and then using it as a preinverse, making it straightforward to try out different model structures. Another is to construct a model of the system and then use it to estimate a preinverse in a second step. This method identifies the inverse in the setup it will be used, but leads to a complicated optimization problem. A third option is to model the forward system and then invert it. This method can be understood using standard identification theory in contrast to the ones above, but the model is tuned for the forward system, not the inverse. Models obtained using the various methods capture different properties of the system, and a more detailed analysis of the methods is presented for linear time-invariant systems and linear approximations of block-oriented systems. The theory is also illustrated in examples. When a preinverse is used, the input to the system will be changed, and typically the input data will be different than the original input. This is why the estimation of preinverses is more complicated than for postinverses, and one set of experimental data is not enough. Here, we have shown that identifying a preinverse in series with the system in repeated experiments can improve the inversion performance.

Digital Predistortion of Power Amplifiers for Wireless Applications

2004 Electric distortion
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Release : 2004
Genre : Electric distortion
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Book Synopsis Digital Predistortion of Power Amplifiers for Wireless Applications by : Lei Ding

Download or read book Digital Predistortion of Power Amplifiers for Wireless Applications written by Lei Ding. This book was released on 2004. Available in PDF, EPUB and Kindle. Book excerpt: Digital predistortion is one of the most cost effective ways among all linearization techniques. However, most of the existing designs treat the power amplifier as a memoryless device. For wideband or high power applications, the power amplifier exhibits memory effects, for which memoryless predistorters can achieve only limited linearization performance. In this dissertation, we propose novel predistorters and their parameter extraction algorithms. We investigate a Hammerstein predistorter, a memory polynomial predistorter, and a new combined model based predistorter. The Hammerstein predistorter is designed specifically for power amplifiers that can be modeled as a Wiener system. The memory polynomial predistorter can correct both the nonlinear distortions and the linear frequency response that may exist in the power amplifier. Real-time implementation aspects of the memory polynomial predistorter are also investigated. The new combined model includes the memory polynomial model and the Murray Hill model, thus extending the predistorter's ability to compensate for strong memory effects in the power amplifier. The predistorter models considered in this dissertation include both even- and odd-order nonlinear terms. By including these even-order nonlinear terms, we have a richer basis set, which offers appreciable improvement. In reality, however, the performance of a predistortion system can also be affected by the analog imperfections in the transmitter, which are introduced by the analog components; mostly analog filters and quadrature modulators. There are two common configurations for the upconversion chain in the transmitter: two-stage upconversion and direct upconversion. For a two-stage upconversion transmitter, we design a band-limited equalizer to compensate for the frequency response of the surface acoustic wave (SAW) filter which is usually employed in the IF stage. For a direct upconversion transmitter, we develop a model to describe the frequency-dependent gain/phase imbalance and dc offset. We then develop two methods to construct compensators for the imbalance and dc offset. These compensation techniques help to correct for the analog imperfections, which in turn improve the overall predistortion performance.

Wireless, Networking, Radar, Sensor Array Processing, and Nonlinear Signal Processing

2018-09-03 Technology & Engineering
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Release : 2018-09-03
Genre : Technology & Engineering
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Book Rating : 055/5 ( reviews)

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Book Synopsis Wireless, Networking, Radar, Sensor Array Processing, and Nonlinear Signal Processing by : Vijay Madisetti

Download or read book Wireless, Networking, Radar, Sensor Array Processing, and Nonlinear Signal Processing written by Vijay Madisetti. This book was released on 2018-09-03. Available in PDF, EPUB and Kindle. Book excerpt: Now available in a three-volume set, this updated and expanded edition of the bestselling The Digital Signal Processing Handbook continues to provide the engineering community with authoritative coverage of the fundamental and specialized aspects of information-bearing signals in digital form. Encompassing essential background material, technical details, standards, and software, the second edition reflects cutting-edge information on signal processing algorithms and protocols related to speech, audio, multimedia, and video processing technology associated with standards ranging from WiMax to MP3 audio, low-power/high-performance DSPs, color image processing, and chips on video. Drawing on the experience of leading engineers, researchers, and scholars, the three-volume set contains 29 new chapters that address multimedia and Internet technologies, tomography, radar systems, architecture, standards, and future applications in speech, acoustics, video, radar, and telecommunications. This volume, Wireless, Networking, Radar, Sensor Array Processing, and Nonlinear Signal Processing, provides complete coverage of the foundations of signal processing related to wireless, radar, space–time coding, and mobile communications, together with associated applications to networking, storage, and communications.

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