Optimal nonlinear digital signal processing : a dynamical systems approach
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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This thesis addresses optimal nonlinear digital signal processing problems aimed to improve power efficiency of modern wireless transmission systems. The first part of this thesis is motivated by peak-to-average power ratio reduction of communication signals. The problem is formulated as minimization of a frequency-weighted convex quadratic cost subject to time-domain output amplitude constraints. A new method for converting optimality conditions into finite-latency stable systems generating optimal outputs with arbitrary precision is proposed. The second part contains analysis of the nonlinear distortion introduced into the base-band (discrete-time) input-output dynamics of the communication systems by the (continuous-time) power amplifier nonlinearity. It is shown that when the nonlinearity is represented by a Volterra series model the resulting baseband equivalent model is a series interconnection of a discrete-time Volterra series model, of the same degree and equivalent memory depth, and a linear system. The result suggests a new, analytically motivated, structure of digital pre-distortion (DPD) of power amplifier nonlinearities. The third part of the thesis focuses on analysis and design of digitally implemented pulse-width modulators (DPWM) used as quantizers for power amplifiers in switched-mode operation. A time-domain input-output model of DPWM which offers new insight into nonlinear behavior of this system is developed. A modified Lloyd-Max quantization based algorithm for linearization of the baseband of a DPWM output is proposed.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 181-193).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.