Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Spectral Analysis of Polynomial Nonlinearity with Applications to RF Power Amplifiers | EURASIP Journal on Applied Signal Processing 2004 12 1831-1840 2004 Hindawi Publishing Corporation Spectral Analysis of Polynomial Nonlinearity with Applications to RF Power Amplifiers G. Tong Zhou School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 USA Email gtz@ Raviv Raich School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 USA Email raviv@ Received 1 September 2003 Revised 2 December 2003 The majority of the nonlinearity in a communication system is attributed to the power amplifier PA present at the final stage of the transmitter chain. In this paper we consider Gaussian distributed input signals such as OFDM and PAs that can be modeled by memoryless or memory polynomials. We derive closed-form expressions of the PA output power spectral density for an arbitrary nonlinear order based on the so-called Leonov-Shiryaev formula. We then apply these results to answer practical questions such as the contribution of AM PM conversion to spectral regrowth and the relationship between memory effects and spectral asymmetry. Keywords and phrases nonlinear polynomial power amplifier spectral analysis. 1. INTRODUCTION Power amplifiers PAs are important components of communications systems and are inherently nonlinear. For Example the so-called class AB PAs which are moderately nonlinear are typically employed in wireless base stations and handsets. When a nonconstant modulus signal goes through a nonlinear PA spectral regrowth broadening appears in the output which in turn causes adjacent channel interference ACI . Stringent limits on ACI are imposed by the standard bodies and thus the extent of the PA nonlinearity must be controlled. We are interested in predicting the amount of spectral re growth for a given level of PA nonlinearity. Since more linear PAs are less efficient one may want to maximize nonlinearity and hence optimize efficiency subject .
