Series-Constellation Feature Based Blind Modulation Recognition for Beyond 5G MIMO-OFDM Systems With Channel Fading

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.