带卷积层的 PRNet 用于降低 OFDM 信号的 PAPR

IF 0.3 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IEICE Communications Express Pub Date : 2024-06-11 DOI:10.23919/comex.2024XBL0091

Masaya Ohta;Koichi Kubota

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引用次数: 0

摘要

这项研究利用深度学习来解决正交频分复用（OFDM）中峰均功率比（PAPR）过高的问题，这对无线通信至关重要。虽然可以使用深度学习模型 PAPR 降低网络（PRNet）来抑制 PAPR，但其计算成本巨大。本研究优化了 PRNet 模型的层数，并用卷积层取代了全连接层，以减少计算负荷。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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PRNet with Convolution Layer for PAPR Reduction of OFDM Signals

This research uses deep learning to address the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPR-reducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.

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来源期刊

IEICE Communications Express ENGINEERING, ELECTRICAL & ELECTRONIC-

自引率

33.30%

发文量

114