DFENet:一个多样化的特征提取神经网络,用于提高无线通信系统中自动调制分类的准确性
Ha-Khanh Le1, Van-Phuc Hoang1, Van-Sang Doan2
1Institute of System Integration, Le Quy Don Technical University, Hanoi, Vietnam.
PloS one
|January 21, 2026
概括
一个新的深度学习模型DFENet提高了无线通信中的自动调制分类 (AMC) 准确性. 它使用多种特征提取块来改善信号识别,特别是在低信号噪声比 (SNR) 的情况下.
科学领域:
- 无线通信无线通信
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 自动调制分类 (AMC) 对无线系统至关重要.
- 现有的方法面临着精度和计算复杂性的挑战.
研究的目的:
- 引入DFENet,一个新的卷积神经网络 (CNN) 来改进AMC.
- 为了提高AMC的准确性,使用多种特征提取 (DFE) 块.
主要方法:
- DFENet采用多分支 DFE 块与多尺度过器.
- 从In-phase和Quadrature-phase (IQ) 数据中提取信号特征.
- 使用各种过器大小的卷积层,以防止过和梯度消失.
主要成果:
- 在HisarMod2019数据集上,DFENet实现了82.76%的平均AMC准确度.
- 在较低的SNR (例如,在-20dB下>60%) 证明了高精度.
- 在RadioML2018.01A数据集上,SNR>6dB的精度超过93%.
结论:
- 与最先进的模型相比,DFENet显著提高了AMC准确性.
- 保持合理的计算复杂性和快速执行.
- 在具有挑战性的无线环境中为调制分类提供强大的解决方案.
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