Theoretical and Experimental Studies of a Probabilistic-Based Memoryless PA Linearization Technique

Abstract

This paper studies the performance of a memoryless power amplifier (PA) linearization technique based on a probabilistic approach. This technique employs a nonparametric method to derive a predistorter function, which does not need any parametric modeling and explicit parameter estimation. It only needs to calculate a probabilistic cumulative distribution function (CDF) and a quantile function (an inverse function of the CDF). Histogram and order statistic methods are proposed to perform the calculation. A rigorous analytic formula is derived for the inter-modulation product power (IMPP) of the PA output signal when a finite number of samples as well as a finite number of bins are used to calculate the CDF and the quantile function. The analytic results show that, with the probabilistic-based technique, the IMPP approaches zero as the number of samples approaches infinity and the bin width approaches zero.