A robust digital baseband predistorter constructed using memory polynomials

Power amplifiers (PAs) are inherently nonlinear devices and are used in virtually all communications systems. Digital baseband predistortion is a highly cost-effective way to linearize PAs, but most existing architectures assume that the PA has a memoryless nonlinearity. For wider bandwidth applications such as wideband code-division multiple access (WCDMA) or wideband orthogonal frequency-division multiplexing (W-OFDM), PA memory effects can no longer be ignored, and memoryless predistortion has limited effectiveness. In this paper, instead of focusing on a particular PA model and building a corresponding predistorter, we focus directly on the predistorter structure. In particular, we propose a memory polynomial model for the predistorter and implement it using an indirect learning architecture. Linearization performance is demonstrated on a three-carrier WCDMA signal.

I. Introduction

power amplifiers (PAs) are indispensable components in a communication system and are inherently nonlinear. It is well known that there is an approximate inverse relationship between the PA efficiency and its linearity. Hence, nonlinear PAs are desirable from an efficiency point of view. The price paid for higher efficiency is that nonlinearity causes spectral regrowth (broadening), which leads to adjacent channel interference. It also causes in-band distortion, which degrades the bit-error rate (BER) performance. Newer transmission formats, such as code-division multiple access (CDMA) and orthogonal frequency-division multiplexing (OFDM), are especially vulnerable to PA nonlinearities, due to their high peak-to-average power ratio, corresponding to large fluctuations in their signal envelopes. In order to comply with spectral masks imposed by regulatory bodies and to reduce BER, PA linearization is necessary.