Deep Learning Autoencoders for Reducing PAPR in Coherent Optical Systems

TL;DR

This paper introduces a deep learning autoencoder approach to reduce PAPR in coherent optical systems, outperforming traditional methods in PAPR reduction, BER performance, and robustness without requiring side information.

Contribution

The paper presents a novel autoencoder-based method for PAPR reduction in optical systems that simplifies the process and improves performance over conventional techniques.

Findings

Achieves over 10 dB PAPR reduction

Surpasses traditional SLM in BER performance

Demonstrates robustness against noise and nonlinear distortions

Abstract

This paper presents an innovative approach to mitigating the peak-to-average power ratio (PAPR). The proposed method uses a deep learning model called autoencoders (AEs) to simplify the process and avoid the complex calculations of traditional methods such as selective mapping (SLM). Unlike SLM, our approach does not need side information about the PAPR distribution. Through simulations of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems, the AE-based model offers substantial enhancements in both PAPR reduction and bit error rate (BER) performance when compared to conventional techniques. An error-free transmission can be acheived with a reduction in PAPR exceeding 10 dB compared to the original signal and a 1 dB advantage over SLM. In particular, the AE model achieves the best BER performance of \(2 \times 10^{-6}\) at 44 dB OSNR, surpassing traditional methods. Furthermore, the model demonstrates robustness against noise and nonlinear distortions, making it appropriate for optical channels experiencing diverse levels of impairment. This innovative technique has the potential to revolutionize next-generation optical communication systems by enabling efficient and reliable data transmission.