A Hammerstein predistortion linearization design based on the indirect learning architecture

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 the PAs, but most existing architectures assume that the PA has a memoryless nonlinearity. For wider bandwidth applications such as WCDMA, PA memory effects can no longer be ignored, and memoryless predistortion has limited effectiveness. In this paper, we model the PA as a Wiener system and construct a Hammerstein predistorter, obtained using an indirect learning architecture. Linearization performance is demonstrated on a 3-carrier UMTS signal.