Large Intelligent Surface Aided Physical Layer Security Transmission
Biqian Feng, Yongpeng Wu, Mengfan Zheng, Xiang-Gen Xia, Yongjian Wang, and Chengshan Xiao

TL;DR
This paper explores how large intelligent surfaces can enhance physical layer security by optimizing secrecy rates through novel algorithms, demonstrating significant improvements over traditional systems in various channel conditions.
Contribution
It introduces two optimization frameworks for LIS-assisted secure transmission, addressing non-convex constraints and expectation calculations, and provides analysis for special scenarios.
Findings
Proposed algorithms effectively maximize secrecy rates.
LIS significantly improves security performance.
Algorithms outperform conventional systems without LIS.
Abstract
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…
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