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

TL;DR

This paper proposes a convolutional autoencoder architecture for reducing PAPR in OFDM systems, integrating a PAPR reduction block with a high power amplifier model, optimized through gradual loss learning.

Contribution

It introduces a novel CAE-based approach for PAPR reduction in OFDM, combining waveform design and amplifier modeling with multi-objective optimization.

Findings

Reduced PAPR compared to traditional algorithms

Maintained acceptable BER levels

Improved spectral response

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.