A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed.Compared to the existing polynomial predistorter,on the one hand,the proposed predistorter based on thedirect learning ar...A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed.Compared to the existing polynomial predistorter,on the one hand,the proposed predistorter based on thedirect learning architecture is more robust to initial conditions of the tap coefficients than that based on in-direct learning architecture;on the other hand,by using two polynomial coefficient combinations,differ-ent polynomial coefficient combination can be selected when the input signal amplitude changes,whicheffectively decreases the estimate error.This paper introduces the direct learning architecture and givesthe dynamic coefficient polynomial expression.A simplified nonlinear recursive least-squares(RLS)algo-rithm for polynomial coefficient estimation is also derived in detail.Computer simulations show that theproposed predistorter can attain 31 dB,28dB and 40dB spectrum suppression gain when our method is ap-plied to the traveling wave tube amplifier(TWTA),solid state power amplifier(SSPA)and polynomialpower amplifier(PA)model,respectively.展开更多
We investigate numerical approximations based on polynomials that are orthogonal with respect to a weighted discrete inner product and develop an algorithm for solving time dependent differential equations.We focus on...We investigate numerical approximations based on polynomials that are orthogonal with respect to a weighted discrete inner product and develop an algorithm for solving time dependent differential equations.We focus on the family of super Gaussian weight functions and derive a criterion for the choice of parameters that provides good accuracy and stability for the time evolution of partial differential equations.Our results show that this approach circumvents the problems related to the Runge phenomenon on equally spaced nodes and provides high accuracy in space.For time stability,small corrections near the ends of the interval are computed using local polynomial interpolation.Several numerical experiments illustrate the performance of the method.展开更多
基金the National High Technology Research and Development Programme of China(No2006AA01Z270)Beijing Jiaotong University Talent Foundation(No2007RC022)
文摘A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed.Compared to the existing polynomial predistorter,on the one hand,the proposed predistorter based on thedirect learning architecture is more robust to initial conditions of the tap coefficients than that based on in-direct learning architecture;on the other hand,by using two polynomial coefficient combinations,differ-ent polynomial coefficient combination can be selected when the input signal amplitude changes,whicheffectively decreases the estimate error.This paper introduces the direct learning architecture and givesthe dynamic coefficient polynomial expression.A simplified nonlinear recursive least-squares(RLS)algo-rithm for polynomial coefficient estimation is also derived in detail.Computer simulations show that theproposed predistorter can attain 31 dB,28dB and 40dB spectrum suppression gain when our method is ap-plied to the traveling wave tube amplifier(TWTA),solid state power amplifier(SSPA)and polynomialpower amplifier(PA)model,respectively.
文摘We investigate numerical approximations based on polynomials that are orthogonal with respect to a weighted discrete inner product and develop an algorithm for solving time dependent differential equations.We focus on the family of super Gaussian weight functions and derive a criterion for the choice of parameters that provides good accuracy and stability for the time evolution of partial differential equations.Our results show that this approach circumvents the problems related to the Runge phenomenon on equally spaced nodes and provides high accuracy in space.For time stability,small corrections near the ends of the interval are computed using local polynomial interpolation.Several numerical experiments illustrate the performance of the method.
