Direct spread spectrum communication has the advantages of strong anti-jamming ability and low probability of interception, which plays an essential role in both civil and military communications. The detection of direct sequence spread spectrum (DSSS) signal becomes very difficult because of its low power spectral density. In this paper, the detection of DSSS signals under non-cooperative conditions is carried out based on the classification technology of deep learning. Firstly, a detection scheme based on convolutional neural network (CNN) is proposed, in which the neural network is used to learn the features of DSSS signal and noise automatically without extracting the features in advance. In addition, we also propose a hybrid CNN-correlation (CORR)-based detection scheme in order to reduce the computational complexity. In this scheme, the autocorrelation of the received signal is truncated and used as the input of the neural network for training and inference. A large number of simulation results show that the detection performance of the proposed two schemes is significantly better than the traditional autocorrelation-based detection method in various scenarios.

中文翻译：

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基于深度学习的直接序列扩频信号检测

直接扩频通信具有抗干扰能力强、被截获概率低等优点，在民用和军用通信中都发挥着至关重要的作用。由于直接序列扩频(DSSS)信号的功率谱密度低，检测变得非常困难。本文基于深度学习的分类技术进行非合作条件下DSSS信号的检测。首先，提出了一种基于卷积神经网络（CNN）的检测方案，该方案利用神经网络自动学习DSSS信号和噪声的特征，而不需要预先提取特征。此外，我们还提出了一种基于混合 CNN 相关（CORR）的检测方案，以降低计算复杂度。在这个方案中，接收信号的自相关被截断并用作神经网络的输入进行训练和推理。大量仿真结果表明，所提两种方案的检测性能在各种场景下均明显优于传统的基于自相关的检测方法。