Secret Key Generation Scheme Based on Generative Adversarial Networks in FDD Systems

被引：5

作者：

Hou, Zongyue ^{[1
]}

Zhang, Xinwei ^{[1
]}

机构：

[1] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China

来源：

IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM WKSHPS 2021) | 2021年

基金：

中国国家自然科学基金;

关键词：

Deep learning; boundary equilibrium generative adversarial network; key generation; frequency division duplexing; CSI;

D O I：

10.1109/INFOCOMWKSHPS51825.2021.9484457

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In frequency division duplexing (FDD) systems, the uplink and downlink transmit information in different frequency bands, so it is difficult to use channel reciprocity to generate secret keys. Existing key generation methods for FDD systems have excessive overhead and security problems. This paper uses deep learning to predict the downlink channel state information (CSI) from the uplink CSI, so that two users can generate highly similar downlink CSI in FDD systems. We propose a key generation scheme based on boundary equilibrium generative adversarial network (BEGAN), including channel estimation, reciprocal channel feature construction, quantization, information reconciliation and privacy amplification. Numerical simulation results are presented to verify the feasibility and effectiveness of the proposed scheme.

引用

页数：6

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