Machine Learning-Based Secret Key Generation for IRS-assisted Multi-antenna Systems

TL;DR

This paper proposes a deep neural network approach to optimize IRS-assisted physical-layer key generation in multi-antenna systems, significantly improving secret key rates by leveraging channel randomness and addressing complex optimization challenges.

Contribution

It introduces a novel unsupervised DNN-based algorithm for joint optimization of BS precoding and IRS reflection, enhancing secret key generation performance in correlated channel environments.

Findings

DNN-based algorithm outperforms benchmark methods in SKR

Closed-form SKR expression derived considering channel correlation

Joint optimization improves key generation efficiency

Abstract

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS precoding matrix and the IRS reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.