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

2020/11/12 by Yara Huleihel, Huleihel, Yara, Eilam Ben-Dror +3 · 2 citations
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Optical Network Technologies #Optical Wireless Communication Technologies #PAPR reduction in OFDM #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2011.06349

openalex publication_date 2020/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• High Performance Signal Design for Optical OFDM Systems using Variational Autoencoder
• RADE: A Neural Codec for Transmitting Speech over HF Radio Channels

Related

• Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder
• Deep Learning Autoencoders for Reducing PAPR in Coherent Optical Systems
• High Performance Signal Design for Optical OFDM Systems using Variational Autoencoder
• Learning OFDM Waveforms with PAPR and ACLR Constraints
• FFT-Free PAPR Reduction Methods for OFDM Signals