End-to-End Waveform Design for Nonlinear Satellite Links with a Convolutional Autoencoder

Yara Huleihel, rom hirsch, Haim H. Permuter

Keywords: Deep learning, Single Carrier, Waveform Design, Satellite Communication

TL;DR: A learning-based approach to waveform design achieves robust performance in nonlinear satellite links without explicit predistortion.

Abstract: We present a convolutional autoencoder (CAE) for single-carrier (SC) waveform design in satellite links with nonlinear high-power amplifiers (HPAs). The CAE jointly optimizes bit-error rate (BER), peak-to-average power ratio (PAPR), and adjacent-channel power ratio (ACPR) via an augmented-Lagrangian objective. Using measured block-upconverter (BUC) data with AM/AM and AM/PM distortions across temperature and frequency variations, we show consistent improvements over conventional PAPR-reduction techniques and baselines, with strong generalization across frequency and temperature variations. Experiments with 64-QAM demonstrate BER and spectral advantages at practical back-off levels without explicit digital predistortion (DPD).

Submission Number: 45