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基于确定性过采样的不平衡航空通信信号调制识别
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TN911.7

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国家自然科学基金(62276206);航空科学基金(2023Z071081001)


A Signal Modulation Classification for Unbalanced Aviation Communication Signals Based on Deterministic Oversampling
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    摘要:

    针对航空通信中,复杂电磁环境下的调制信号分类任务数据不平衡以及少数类信号样本的缺乏导致分类器的性能下降的问题,提出了一种基于确定性过采样技术的不平衡航空通信调制信号分类方法。该方法通过合成少数类信号样本,平衡数据集,从而减少数据不平衡对分类器的负面影响。基于RadioML 2016.10a数据集,选择11种调制方式,在-8 dB、-4 dB、0 dB、4 dB、8 dB信噪比下构建了4种不平衡场景进行验证。实验结果表明,相较于不平衡数据集,文中所提方法在Msmcnet、Resnet50和Densenet121 3种网络模型上,分类准确率分别提高了2.78%、0.92%、3.45%。与传统的SMOTE方法相比,所提方法在处理多类不平衡问题上表现出更好的分类性能。该方法能够有效提高调制信号分类任务中的准确率,尤其是在复杂电磁环境下的航空通信场景中。

    Abstract:

    In aeronautical communications, because of that data are imbalance, and being lack of minority class signal samples, there is a drop in the classifier performance in modulation signal classification tasks under complex electromagnetic environments, this paper proposes a classification method for unbalanced modulation signals based on deterministic oversampling. The method synthesizes minority class signal samples to balance the dataset and reduce the impact of data imbalance on classification performance. Based on the method at the RadioML 2016.10a dataset, 11 modulation types are selected under 5 signal-to-noise ratios (-8 dB, -4 dB, 0 dB, 4 dB, 8 dB) and 4 imbalance scenarios are constructed. The experimental results show that compared to the imbalanced dataset, the proposed method improves classification accuracy by 2.78%, 0.92% and 3.45% on MsmcNet, ResNet50, and DenseNet121 network models respectively, and compared to the traditional SMOTE method, the proposed method demonstrates still better performance in handling multi-class imbalance problems. And this method is enabled to effectively improve the accuracy in modulation signal classification in complex aeronautical communication environments, especially under complex electric-magnetic environments.

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李浩然, 王艺然, 白静, 肖竹.基于确定性过采样的不平衡航空通信信号调制识别[J].空军工程大学学报,2025,26(3):18-25

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  • 在线发布日期: 2025-06-04
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