Secret Key Generation for FDD Systems Based on Complex-Valued Neural Network

Xinwei Zhang, Guyue Li, Zongyue Hou, Aiqun Hu

Abstract: Secret key generation based on wireless channel reciprocity has received widespread attention. However, in frequency division duplexing (FDD) systems, since the carrier frequencies of the uplink and downlink are different and the channel coefficients are no longer reciprocal, key generation for FDD systems is challenging. In this paper, a Complex-Valued neural Network (CVNet) is proposed to predict the downlink channel and generate reciprocal channel characteristics. Then, based on the trained CVNet, we propose a key generation protocol for FDD systems. Numerical results show that the CVNet achieves better performance in terms of prediction accuracy, bit disagreement rate, and bit generation ratio than a traditional Real-Valued Network (RVNet) under high signal-to-noise ratios. Furthermore, the training parameters required by the CVNet account for only half of that required by the RVNet.