Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the cognitive radio (CR) technique is indispensable for spectrum management and can put the unutilized spectrum to good use. As the core technology of CR, blind modulation recognition (BMR) plays a pivotal role in improving spectral efficiency. However, the BMR research on MIMO-OFDM systems still lacks enough attention. Given the prosperity of deep learning, we propose a series-constellation multi-modal feature network (SC-MFNet) to recognize the modulation types of MIMO-OFDM subcarriers. Without any prior information, a blind signal separation algorithm is employed to reconstruct the impaired transmitted signal. Considering the insufficient features of signal series, we propose a segment accumulated constellation diagram (SACD) strategy to produce the striking constellation features. Moreover, the proposed multi-modal feature fusion network is employed to collect the advantages of series and SACD features, which are extracted by one-dimensional convolution (Conv1DNet) branch and improved EfficientNet branch, respectively. Experimental results demonstrate that in a realistic non-cooperative cognitive communication scenario where prior information is exempted, the proposed SC-MFNet outperforms the traditional feature-based methods and the state-of-the-art neural networks which are based on either constellation features or series features.

中文翻译：

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针对具有信道衰落的超 5G MIMO-OFDM 系统的基于系列星座特征的盲调制识别


由于当前5G和即将到来的6G系统中无线电频谱的短缺，认知无线电（CR）技术对于频谱管理是必不可少的，可以充分利用未利用的频谱。盲调制识别（BMR）作为CR的核心技术，对于提高频谱效率起着至关重要的作用。然而，MIMO-OFDM系统的BMR研究仍然缺乏足够的重视。鉴于深度学习的繁荣，我们提出了一种系列星座多模态特征网络（SC-MFNet）来识别 MIMO-OFDM 子载波的调制类型。在没有任何先验信息的情况下，采用盲信号分离算法来重建受损的发射信号。考虑到信号序列特征不足，我们提出了分段累积星座图（SACD）策略来产生引人注目的星座特征。此外，所提出的多模态特征融合网络用于收集系列和SACD特征的优点，这些特征分别由一维卷积（Conv1DNet）分支和改进的EfficientNet分支提取。实验结果表明，在排除先验信息的现实非合作认知通信场景中，所提出的 SC-MFNet 优于传统的基于特征的方法和基于星座特征或系列特点。