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arXiv · 2011.06349

Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder

Yara Huleihel·Eilam Ben-Dror·Haim H. Permuter
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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 analyze the models performance by examining the bit error rate (BER), the PAPR and the spectral response, and comparing them with common PAPR reduction algorithms.

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Yara Huleihel, Eilam Ben-Dror, Haim H. Permuter. 2020-11-12. Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder. https://arxiv.org/abs/2011.06349

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