Blind Signal Recognition Method of STBC Based on Multi-channel Convolutional Neural Network

Blind signal recognition (BSR) is a significant research topic in the field of intelligent signal processing. However, existing BSR of space-time block codes (STBC) mainly depends on conventional algorithms, which require priori information and can only identify a relatively limited amount of STBC. Although deep learning (DL) has been widely used in signal recognition, so far there are few studies on BSR of STBC in multiple-input multiple-output (MIMO) systems using DL. In this paper, a blind recognition approach for STBC based on multi-channel convolutional neural network (MCNN) is proposed. By leveraging the structure of multiple input channel, the in-phase and quadrature (IQ) channel information of STBC signals can be comprehensively extracted. Simulation results demonstrate that the proposed algorithm extends the recognizable STBC codes to 6, and can also improve the recognition accuracy in comparison to traditional convolutional neural network (CNN). The model proposed in this paper has been validated with two datasets and experimentally proved to be well generalized.