A Neural Network Pre-Distorter for the Compensation of HPA Nonlinearity: Application to Satellite Communications

Rafik Zayani, Ridha Bouallegue
2007 2007 4th IEEE Consumer Communications and Networking Conference  
Neural networks (NNs) are able to give solutions to complex problems in digital communications due to their nonlinear processing, parallel distributed architecture, self-organization, capacity of learning and generalization, and efficient hardware implementation. The pre-distortion being at the center of interest of this paper is one of the possible methods to compensate for HPA nonlinearities. The principle of pre-distortion is to distort the HPA input signal by an additional device called a
more » ... e-distorter whose characteristics are the inverse of those of the amplifier. In this paper, we propose a pre-distortion scheme based on a feed-forward neural network. Efficient High Power Amplifiers (HPA) present non-linearities generating amplitude and phase distortions on the HPA output signal; the proposed pre-distortion technique will reduce theses distortions. The performance of the proposed scheme is examined through computer simulations for 16-QAM OFDM signals. It is confirmed that the proposed pre-distorter with neural network consisting with one hidden layer and nine neurons gives a good performance improvement of quality of the transmission. Specifically, improvements in the reduction of the bit error rate (BER) are demonstrated for the travelling wave tube (TWT) HPA model.
doi:10.1109/ccnc.2007.98 dblp:conf/ccnc/ZayaniB07 fatcat:hzuj45eq45c5tkcheqj2lf2nla