  In this paper, we investigate a large intelligent surface-enhanced
(LIS-enhanced) system, where a LIS is deployed to assist secure transmission.
Our design aims to maximize the achievable secrecy rates in different channel
models, i.e., Rician fading and (or) independent and identically distributed
Gaussian fading for the legitimate and eavesdropper channels. In addition, we
take into consideration an artificial noise-aided transmission structure for
further improving system performance. The difficulties of tackling the
aforementioned problems are the structure of the expected secrecy rate
expressions and the non-convex phase shift constraint. To facilitate the
design, we propose two frameworks, namely the sample average approximation
based (SAA-based) algorithm and the hybrid stochastic projected
gradient-convergent policy (hybrid SPG-CP) algorithm, to calculate the
expectation terms in the secrecy rate expressions. Meanwhile, majorization
minimization (MM) is adopted to address the non-convexity of the phase shift
constraint. In addition, we give some analyses on two special scenarios by
making full use of the expectation terms. Simulation results show that the
proposed algorithms effectively optimize the secrecy communication rate for the
considered setup, and the LIS-enhanced system greatly improves secrecy
performance compared to conventional architectures without LIS.
