Signal detection with co-channel interference using deep learning

Signal detection using deep learning is a challenging and promising research topic. Several learning-based signal detectors have been proposed to produce significant results. However, most of them ignored interference in their designs. In this paper, we evaluate the performance presence co-channel under different channel conditions. Specifically, fully connected neural network (FCDNN) convolutional (CNN) are examined as data-driven detector for blind without knowledge state information. important system parameters, including signal-to-interference ratio, number interferences type interference, considered. Numerical results show that FCDNN CNN-based better robustness SIRs conditions than traditional performs CNN when SIR small order modulation high.