An adaptive neural network pre-distorter for non stationary HPA in OFDM systems

It is well known that HPAs (High Power Amplifiers) are inherently nonlinear devices. Hence, many researches have focused on the pre-distortion of memoryless stationary HPAs. However HPAs can no longer be considered as stationary in a real satellite system. In fact, if the amplifier exhibit nonlinear characteristics constant in time, which is a reasonable assumption in many low power cases, a fixed pre-distorter is enough to achieve a good linear performance. However, power amplifiers operating under more stringent conditions may undergo slow but significant changes in their AM/AM and AM/PM characteristics basically due to factors like temperature, age of components, power level, biasing variations, frequency changes and so on. In this paper, we present an adaptive pre-distortion technique based on a feed-forward neural network that makes it possible to compensate the nonlinearities of an HPA with taken into consideration the time variations of HPA characteristics. We use an indirect approach that calculates a post-distortion system applied as a pre-distortion. The performance of the proposed scheme is examined through computer simulations for 16-QAM OFDM signals.