  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.
