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

使用深度学习进行信号检测是具有挑战性和前途的研究主题。已经提出了几种基于学习的信号检测器来产生显着的结果。但是，他们中的大多数人都忽略了设计中的干扰。在本文中，我们评估了在不同信道条件下存在同信道干扰的情况下基于学习的信号检测器的性能。具体来说，在不了解信道状态信息的情况下，全连接式深度神经网络（FCDNN）和卷积神经网络（CNN）被作为用于盲信号检测的数据驱动信号检测器进行了检查。考虑了几个重要的系统参数，包括信噪比，干扰数量和干扰类型。