A new Volterra predistorter based on the indirect learning architecture

Nonlinear compensation techniques are becoming increasingly important. We present a new Volterra-based predistorter, which utilizes the indirect learning architecture to circumvent a classical problem associated with predistorters, namely that the desired output is not known in advance. We utilize the indirect learning architecture and the recursive least square (RLS) algorithm. Specifically, we propose an indirect Volterra series model predistorter which is independent of a specific nonlinear model for the system to be compensated. Both 16-phase shift keying (PSK) and 16-quadrature amplitude modulation (QAM) are used to demonstrate the efficacy of the new approach