基于神经网络的移动通信系统PAPR降低技术

IF 1.3 4区工程技术 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI:10.1109/TLA.2025.11045671

Bianca S. de C. da Silva;Pedro H. C. de Souza;Luciano L. Mendes

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引用次数: 0

摘要

本文提出了一种利用神经网络降低OFDM系统PAPR的新方法。神经网络利用由MCSA生成的训练数据集，该数据集对神经网络进行微调，以获得与MCSA相似的PAPR减少。与传统技术（如PTS）相比，该解决方案通过实现高达4 dB的PAPR降低，提供了卓越的性能。然而，一个显著的优势是，与MCSA相比，训练后的NN具有较低的计算复杂度，而不会影响其PAPR减少能力

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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PAPR Reduction Technique for Mobile Communication Systems Using Neural Networks

This work proposes a new solution to reduce the PAPR in OFDM systems using NN. The NN leverages a training dataset generated by the MCSA, which fine-tunes the NN for attaining a similar PAPR reduction of the MCSA. Compared to traditional techniques such as the PTS, the proposed solution offers superior performance by achieving a PAPR reduction of up to 4 dB. Nevertheless, a significant advantage is that the trained NN presents a lower computational complexity compared to the MCSA, without compromising its PAPR reduction capabilities

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来源期刊

IEEE Latin America Transactions COMPUTER SCIENCE, INFORMATION SYSTEMS-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

3.50

自引率

7.70%

发文量

192

审稿时长

3-8 weeks

期刊介绍： IEEE Latin America Transactions (IEEE LATAM) is an interdisciplinary journal focused on the dissemination of original and quality research papers / review articles in Spanish and Portuguese of emerging topics in three main areas: Computing, Electric Energy and Electronics. Some of the sub-areas of the journal are, but not limited to: Automatic control, communications, instrumentation, artificial intelligence, power and industrial electronics, fault diagnosis and detection, transportation electrification, internet of things, electrical machines, circuits and systems, biomedicine and biomedical / haptic applications, secure communications, robotics, sensors and actuators, computer networks, smart grids, among others.