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