School of Information Systems Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
Sensors (Basel). 2022 Aug 30;22(17):6539. doi: 10.3390/s22176539.
Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8dB.
调制识别是信号截获分析中不可或缺的一部分，一直是无线通信领域的研究热点。随着电磁频谱环境的日益复杂，信号传播中的干扰越来越严重。本文提出了一种基于多模态特征融合的调制识别方案，旨在提高不同信道下调制识别的性能。首先，在信号预处理阶段，提取不同的时频域特征作为网络输入。采用带通道门限的残差收缩构建单元（RSBU-CW）构建深度卷积神经网络，提取空间特征，并与 LSTM 提取的时间特征进行两两交互，增加特征的多样性。最后，采用 PNN 模型对网络提取的特征进行交叉融合，增强特征之间的互补性。仿真结果表明，所提出的方案比现有的特征融合方案具有更好的识别性能，并且在多径衰落信道中也能取得良好的识别性能。在公共数据集 RadioML2018.01A 上的测试结果表明，当信噪比（SNR）达到 8dB 时，识别准确率超过 95%。
