论文标题
RIS协助的安全传输利用了Eavesdropper的统计CSI
RIS-Assisted Secure Transmission Exploiting Statistical CSI of Eavesdropper
论文作者
论文摘要
我们研究了可重新配置的智能表面（RIS）辅助下行链路安全变速器，仅窃听的统计通道可用。为了解决随机的阵尾保密率（ESR）最大化问题，得出了ESR（LESR）的确定性下限。我们的目标是通过在接入点（AP）共同设计发射光束，并通过RIS处的相移反射光束形成，来最大化LESR。为了解决非凸LESR最大化问题，我们基于惩罚双重分解（PDD）优化框架开发了一种新颖的惩罚双凸近近似（PDCA）算法，在该框架中，严格的限制因素被惩罚并将目标置于目标函数中，将其作为增强的lagrangian组成部分。所提出的PDCA算法执行双回路迭代，即内部循环依次到块连续的凸近近似值（BSCA）以更新优化变量；当外循环调整了增强拉格朗日成本功能的拉格朗日乘数和惩罚参数时。与计算复杂性低的计算复杂性可以保证与Karush-Kuhn-Tucker（KKT）解决方案的收敛性。仿真结果表明，所提出的PDCA方案优于与EavesDropper的统计通道知识的普遍采用的交替优化（AO）方案。
We investigate the reconfigurable intelligent surface (RIS) assisted downlink secure transmission where only the statistical channel of eavesdropper is available. To handle the stochastic ergodic secrecy rate (ESR) maximization problem, a deterministic lower bound of ESR (LESR) is derived. We aim to maximize the LESR by jointly designing the transmit beamforming at the access point (AP) and reflect beamforming by the phase shifts at the RIS. To solve the non-convex LESR maximization problem, we develop a novel penalty dual convex approximation (PDCA) algorithm based on the penalty dual decomposition (PDD) optimization framework, where the exacting constraints are penalized and dualized into the objective function as augmented Lagrangian components. The proposed PDCA algorithm performs double-loop iterations, i.e., the inner loop resorts to the block successive convex approximation (BSCA) to update the optimization variables; while the outer loop adjusts the Lagrange multipliers and penalty parameter of the augmented Lagrangian cost function. The convergence to a Karush-Kuhn-Tucker (KKT) solution is theoretically guaranteed with low computational complexity. Simulation results show that the proposed PDCA scheme is better than the commonly adopted alternating optimization (AO) scheme with the knowledge of statistical channel of eavesdropper.
