Thèse de doctorat
Résumé : Satellite communication system designers are continuously struggling to improve the channel capacity. A critical challenge results from the limited power available aboard the satellite.Because of this constraint, the onboard power amplifier must work with a small power supply which limits its maximum output power. To ensure a sufficient Signal-to-Noise power Ratio (SNR) on the receiver side, the power amplifier must work close to its saturation point. This is power efficient but unfortunately adds non-linear distortions to the communication channel. The latters are very penalizing for high order modulations.In the literature, several equalization algorithms have been proposed to cope with the resulting non-linear communication channel. The most popular solution consists in using baseband Volterra series in order to build non-linear equalization filters. On the other hand, the Recurrent Neural Networks (RNNs), which come from the artificial neural network field, are also interesting candidates to generate such non-linear filters. But they are difficult to implement in practice due to the high complexity of their training. To simplify this task, the Echo State Network (ESN) paradigm has been proposed. It has the advantage of offering performances similar to classical RNNs but with a reduced complexity.The purpose of this work is, first, to compare this solution to the state-of-the-art baseband Volterra filters. We show that the classical ESN is able to reach the same performances, evaluated in terms of Bit Error Rate (BER), and has similar complexity. Secondly, we propose a new design for the ESN which achieves a strong reduction in complexity while conserving a similar BER.To compensate for the channel, the literature proposes to adapt the coefficients of these equalizers with the help of a training sequence in order to recover the transmitted constellation points. We show that, in such a case, the usual symbol detection criterion, based on Euclidean distances, is no longer optimal. For this reason, we first propose a new detection criterion which meets the Maximum Likelihood (ML) criterion. Secondly, we propose a modification of the equalizers training reference points in order to improve their performances and make the detection based on Euclidean distances optimal again. This last solution can offer a significant reduction of the BER without increasing the equalization and detection complexity. Only the new training reference points must be evaluated.In this work, we also explore the field of analog equalizers as different papers showed that the ESN is an interesting candidate for this purpose. It is a promising approach to reduce the equalizer complexity as the digital implementation is very challenging and power-hungry, in particular for high bandwidth communications. We numerically demonstrate that a dedicated analog optoelectronic implementation of the ESN can reach the state-of-the-art performance of digital equalizers. In addition, we show that it can reduce the required resolution of the Analog-to-Digital Converters (ADCs).Finally, a hardware demonstration of the digital solutions is proposed. For this purpose, we build a physical layer test bench which depicts a non-linear communication between two radios. We show that if we drive the transmitter power amplifier close to its saturation point, we can improve the communication range if the non-linear distortions are compensated for at the receiver. The transmitter and the receiver are implemented with Software Defined Radios (SDRs).