Paper data
Title:
Predistorsion of non-linear satellite channels using neural network: architecture, algorithm and implementation
Author(s):
Langlet Fabien, TéSA
Abdulkader Hasan,
Roviras Daniel,
Page numbers in the proceedings:
Volume II pp 53-56
Session:
Non linear Techniques for Channel Equalization (1/2)
Predistorsion of non-linear satellite channels using neural network: architecture, algorithm and implementation
Author(s):
Langlet Fabien, TéSA
Abdulkader Hasan,
Roviras Daniel,
Page numbers in the proceedings:
Volume II pp 53-56
Session:
Non linear Techniques for Channel Equalization (1/2)
Paper abstract
This paper presents the adaptive linearisation of a non-linear digital satellite communication down link. That link is made up a 16-QAM modulator, followed by a non-linear High Power Amplifier, on board the satellite. When using the amplifier with low input back-off for a maximum power efficiency, two kinds of distortions occur on the input signal: amplitude (AM/AM conversion) and phase (AM/PM conversion). The satellite payload is regenerative. So, we use a predistortion on board to linearize the amplifier. We present the predistortion architecture realized with Multi-Layer Perceptron (MLP) Neural Networks (NN). Two algorithms associated to that predistorter are shown and compared: the ordinary and the natural gradient. The major problem to implement that predistorter is to get enough bandwidth (100 Mbits/s data rate). A mixed analog/digital implementation is one solution to solve it. We analyze the implementation imperfections effects in comparison with the theoretical algorithm.
Paper
