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

arXiv: https://arxiv.org/abs/2011.06349 · HTML: https://mlsanity.com/paper/2011.06349/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder](https://mlsanity.com/paper/2301.05017/) (2023)

## Abstract (arXiv)

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.