Adaptive pre-distortion techniques based on orthogonal polynomials

Abstract

Pre-distortion in digital baseband is a cost-effective method to linearise the transmit power amplifiers of spectrally efficient communication systems. From a signal processing point of view, the functional structure of the pre-distorter is primarily determined by the decision which model to choose, as well as by the selected adaptive algorithm. During the last two decades, in literature, a multitude of pre-distorter structures has been proposed and analysed. In this work, we focus on simple and commonly employed models (the Wiener model and the Hammerstein model) consisting of a linear filter and a static nonlinearity. The latter is represented using a basis of orthogonal polynomials. First, applying practical transmission signals, different orthogonal polynomial bases are compared with respect to the numerical condition of least squares estimation, and with respect to the convergence behaviour of gradient methods. In a second step, pre-distorters which employ orthogonal polynomials are adapted by the indirect learning structure, respectively, the nonlinear filtered-x least mean squares algorithm. Based on simulations and burst measurements with a commercial power amplifier, the real-world performance of such pre-distortion systems is investigated.