Machine Learning-Assisted Wireless PHY Key Generation with Reconfigurable Intelligent Surfaces
Long Jiao, Guohua Sun, Junqing Le, Kai Zeng
Abstract
The key generation rate (KGR) performance of wireless physical layer (PHY) key generation can be limited by the quasi-static slow fading environment. In this work, we aim to exploit the radio environment reconfiguration ability enabled by reconfigurable intelligent surface (RIS) to improve KGR of PHY key generation. By rapidly changing the RIS configurations, the randomness or entropy rate of the wireless channel can be significantly increased, thus improving the KGR. To achieve high KGR while keeping low bit disagreement ratio (BDR), for the first time, we propose a machine learning (ML) based adaptive quantization level prediction scheme to decide an optimal quantization level based on channel state information (CSI). Simulation results show that with a prediction accuracy as high as 98.2%, the proposed ML-based prediction model tends to assign high quantization levels in the high SNR regime to reduce BDR, while adopting low quantization levels under low SNRs to maintain a low BDR.