On Physical-Layer Authentication via Triple Pool Convolutional Neural Network
Applied Data Science
2020 IEEE Globecom Workshops (GC Wkshps)
This paper introduces a novel physical-layer authentication scheme, called Triple Pool Convolutional Neural Network physical-layer authentication (TP-CNN-PHA), aiming to enable a lightweight user authentication mechanism based on physical-layer channel state information (CSI). We first introduce the TP-Net, which is characterized by jointly utilizing maximum pooling, average pooling, and global pooling on a globally connected CNN architecture. To assess its performance, we conduct two sets of experiments, including the one using simulated channel data, and the other one utilizing real experiment data generated from our wireless testbed. The result demonstrates the superiority of the proposed TP-CNN-PHA in terms of authentication accuracy and valid complexity reduction compared with all the considered counterparts, including the threshold-based authentication method.
China Scholarship Council
channel state information (CSI), convolutional neural network (CNN), Edge computing, physical-layer authentication
Yi Chen, Shahriar Real, Hong Wen, Boyang Cheng, Wei Wang, Pin Han Ho, and Shih Yu Chang. "On Physical-Layer Authentication via Triple Pool Convolutional Neural Network" 2020 IEEE Globecom Workshops (GC Wkshps) (2020). https://doi.org/10.1109/GCWkshps50303.2020.9367391