@inproceedings{95ba3d28133344c8ba41e31791b757a2,
title = "Low PAPR Waveform Design for OFDM Systems Based on Convolutional Autoencoder",
abstract = "This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design, for orthogonal frequency division multiplexing (OFDM) systems. The proposed architecture integrates a PAPR reduction block and a non-linear high power amplifier (HPA) model. We apply gradual loss learning for multi-objective optimization. We analyse the model' performance by examining the bit error rate (BER), the PAPR and the spectral response, and comparing them with common PAPR reduction algorithms.",
keywords = "Autoencoder, OFDM, PAPR, convolutional neural network, deep learning, wireless signal processing",
author = "Yara Huleihel and Eilam Ben-Dror and Permuter, {Haim H.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020 ; Conference date: 14-12-2020 Through 17-12-2020",
year = "2020",
month = dec,
day = "14",
doi = "10.1109/ANTS50601.2020.9342801",
language = "English",
series = "International Symposium on Advanced Networks and Telecommunication Systems, ANTS",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020",
address = "United States",
}