Work

A Novel PAPR Reduction Scheme Based on Deep Autoencoder Network for FBMC Systems

Year: 2025

Type: article

Abstract: Filter bank multicarrier (FBMC) is a crucial complementary waveform to orthogonal frequency-division multiplexing (OFDM) in future communication systems. However, FBMC systems also suffer from the drawback of an excessively high peak-to-average power ratio (PAPR). Moreover, PAPR reduction methods designed for OFDM systems cannot be directly applied to FBMC systems due to the overlapping nature of FBMC symbols. In this paper, we propose a novel deep learning (DL)-based PAPR reduction scheme for FBMC systems. This innovative approach employs a deep denoising autoencoder (DAE) network in the time domain to suppress the PAPR of FBMC signals and reconstruct the ideal signal. Simulation results demonstrate that the proposed DAE-PAPR scheme achieves a PAPR reduction gain of approximately 2.3 dB to 4.5 dB compared to conventional state-of-the-art methods, while maintaining excellent bit error rate (BER) performance and out-of-band energy leakage characteristics.

Source: IEEE Access

DOI: 10.1109/access.2025.3562066

Authors: Xing Cheng, Deli Chen, Shuaishuai Li, Shanbao He, Dejin Kong

Institutions: Beijing Information Science & Technology University, Wuhan Textile University

Language: English

Topic: PAPR reduction in OFDM

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: National Natural Science Foundation of China, Natural Science Foundation of Anhui Province, Beijing Municipal Natural Science Foundation