中国空间科学技术

中国空间科学技术 ›› 2025, Vol. 45 ›› Issue (6): 170-183.doi: 10.16708/j.cnki.1000-758X.2025.0101

• 论文 • 上一篇    

基于级联融合卷积网络的NTN星地信道识别

范子明1,2，闫毅1,*，范亚楠1，李雪1，姚秀娟1，高翔1   

  1. 1.中国科学院国家空间科学中心，北京100190
    2.中国科学院大学 电子电气与通信工程学院，北京100049
  • 收稿日期:2024-11-20 修回日期:2024-12-17 录用日期:2024-12-21 发布日期:2025-11-17 出版日期:2025-12-01

NTN satellite-terrestrial channel recognition based on cascade merge convolutional network

FAN Ziming1,2，YAN Yi1,*，FAN Yanan1，LI Xue1，YAO Xiujuan1，GAO Xiang1   

  1. 1.National Space Science Center，Chinese Academy of Sciences，Beijing 100190，China
    2.School of Electronic，Electrical and Communication Engineering，University of Chinese Academy of Sciences，Beijing 100049，China
  • Received:2024-11-20 Revision received:2024-12-17 Accepted:2024-12-21 Online:2025-11-17 Published:2025-12-01

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摘要： 空间信道识别是非地面网络（Non-Terrestrial Networks, NTN）通信中的关键技术，为满足不同环境下的通信需求提供先验信息。由于长距离的卫星通信传播环境复杂且动态时变迅速，现有信道识别技术仅利用幅度特征难以精确识别NTN信道类型，导致识别准确率低下。针对此问题，提出了基于信道冲激响应复信号的级联融合卷积网络（Cascade Merge Convolutional Network, CMC-Net）的NTN信道识别方法。在不提升模型参数量的前提下，所提方法充分考虑了IQ两路独立与联合的空时特征，提升了NTN信道识别的性能和信息利用率。首先通过IQ融合卷积模块的一维卷积操作提取信道数据的单路局部空间特征，并通过连接层将两路特征融合，再经过级联卷积模块进一步的特征提取和压缩，随后利用时序分类模块进行全局时序特征提取，最终输出识别结果。仿真结果表明，与使用幅度特征的信道识别方法相比，使用信道IQ数据在CNN、CNNLSTM和MLP分类器的平均识别准确率分别高出0.99%、2.99%和8.25%；CMC-Net对NTN信道的平均识别准确率在0～20dB的信噪比范围内达到98.15%；在-20～20dB的信噪比范围内，平均识别准确率相比LSTM、CNN 、MLP和CNNLSTM分类器分别提高32.77%、4.07%、3.48%和1.26%。由此表明使用复数形式的信道冲激响应可有效提高NTN信道识别准确率，且所提的CMC-Net方法用更少的参数量实现了更准确的NTN信道识别，拓展了信道识别技术在卫星领域的应用。

关键词: 空间信道识别, NTN信道, 深度学习, 空间通信, 卷积神经网络

Abstract: Spatial channel recognition is a crucial technology in Non-Terrestrial Networks (NTN) communication, providing prior information to meet communication needs in different environments. Due to the complex and rapidly changing propagation environment of long-distance satellite communication, existing channel recognition technologies that only use amplitude features struggle to accurately identify NTN channel types, resulting in low recognition accuracy.To address this issue, a Cascade Merge Convolutional Network (CMC-Net) using the channel impulse response complex signal is proposed. This approach captures independent and joint spatiotemporal features of IQ components without increasing model parameters, which improves NTN channel recognition performance and information utilization. Initially, a one-dimensional convolution extracts local spatial features from each IQ component, which are then combined via a concatenation layer followed by further feature extraction and compression via two cascaded convolution modules. Finally, a temporal classification module is employed to capture global temporal features, leading to the recognition result. Simulation results show that, compared to channel recognition methods using amplitude features, the use of channel IQ data improves the average recognition accuracy by 0.99%, 2.99%, and 8.25% for CNN, CNN-LSTM, and MLP classifiers, respectively. The proposed CMC-Net achieves an average recognition accuracy of 98.15% in the 0～20dB SNR range. In the -20～20dB SNR range, the CMC-Net method improves recognition accuracy by 32.77%, 4.07%, 3.48%, and 1.26% compared to LSTM, CNN, MLP, and CNN-LSTM classifiers, respectively.The results demonstrate that using channel impulse response complex signals effectively improves NTN channel recognition accuracy. The proposed CMC-Net method achieves higher accuracy for NTN channel recognition with fewer parameters, which expands the application of channel recognition technology in the satellite domain.

Key words: spatial channel recognition, NTN channels, deep learning, space communication, convolutional neural network