论文标题
基于深度学习和人造噪声的高效无人机物理层安全性
Efficient UAV Physical Layer Security based on Deep Learning and Artificial Noise
论文作者
论文摘要
网络连接的无人机（UAV）通信是实现高速图像传输的常见解决方案。这些无线网络的广播性质使此通信容易窃听。本文认为在存在被动窃听器的情况下，两个节点之间的压缩秘密图像传播问题。在本文中，我们使用自动编码器/解码器卷积神经网络，通过使用深度学习算法，我们可以压缩/解压缩图像。另外，我们使用网络物理层特征来生成高速率人造噪声以保护数据。利用通道的功能和应用人工噪声，降低未经授权用户的通道容量，并防止窃听者检测到接收的数据。我们的仿真实验表明，对于授权节点中SNR的接收数据少于5，MSE小于0.05。
Network-connected unmanned aerial vehicle (UAV) communications is a common solution to achieve high-rate image transmission. The broadcast nature of these wireless networks makes this communication vulnerable to eavesdropping. This paper considers the problem of compressed secret image transmission between two nodes, in the presence of a passive eavesdropper. In this paper, we use auto encoder/decoder convolutional neural networks, which by using deep learning algorithms, allow us to compress/decompress images. Also we use network physical layer features to generate high rate artificial noise to secure the data. Using features of the channel with applying artificial noises, reduce the channel capacity of the unauthorized users and prevent eavesdropper from detecting received data. Our simulation experiments show that for received data with SNR fewer than 5 in the authorized node, the MSE is less than 0.05.
