FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems

The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to identify the signal types in a complex com-munication environment. However,owing to the restrictions on the prior information and channel conditions,these existing algo-rithms cannot perform well under strong interference and non-cooperative communication conditions. To overcome these defects,this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model. Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally,the concatenate fusion,residual block and fully-connected lay-ers are applied to acquire the STBC-OFDM signal types. Simula-tion experiments show that the average recognition probability of the proposed method at ?12 dB is higher than 98％. Com-pared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addi-tion,the proposed deep learning-based model can directly iden-tify the pre-processed FOLMS samples without a priori informa-tion on channel and noise,which is more suitable for non-coope-rative communication systems than the existing algorithms.