Deep-Learning-Based Physical-Layer Lightweight Authentication in Frequency-Division Duplex Channel

Abstract

This letter proposes a lightweight authentication scheme based on secret key generation for frequency-division duplexing. Firstly, a base station predicts downlink channel state information (CSI) from uplink CSI with the aid of deep learning. Then, a secret key is shared between the BS and a mobile user by quantizing the downlink CSI. Since this key generation method uses physical-layer features, the costs of the calculation complexity, the key distribution, and the management, which are typically imposed by the conventional upper-layer key generation, are significantly reduced. Furthermore, the generated key is utilized to carry out low-latency and low-complexity authentication, which is suitable for Internet of things applications.