论文标题
可重新配置的智能表面位置和被动边界成形优化,以最大化保密评价
Joint Reconfigurable Intelligent Surface Location and Passive Beamforming Optimization for Maximizing the Secrecy-Rate
论文作者
论文摘要
对物理层安全性(PLS)进行了研究,以用于可重构的智能表面(RIS)辅助无线网络,在此源在恶意的窃听者的情况下,源将其机密信息传输到合法的目的地。为了使RIS位置约束下的保密率最大化,并确定每个RIS单元的反射系数的模态不大于1。由于产品最小化问题是非convex,因此我们提出了一种用于求解它的两层优化算法。基于获得的接近最佳RIS 3D位置,我们进一步制定了被动边界成形优化问题,然后提议应用Charnes-Cooper转换以及顺序的等级 - 一个约束弛豫(SROCR)算法来解决它。我们的数值结果表明,所提出的J-LPB优化方案的保密率高于基准。明确地,我们使用以下基准:基于近源的RIS位置和被动式波束成形(NSB-LPB)优化方案,基于近后预先预测的RIS位置和无源光束(NDB-LPB)优化方案,以及随机RIS位置和无源和无源和无源和无源仪(R-LPB)优化方案。最后,随着RIS单位的数量,我们的J-LPB计划的好处进一步增加。
The physical layer security (PLS) is investigated for reconfigurable intelligent surface (RIS) assisted wireless networks, where a source transmits its confidential information to a legitimate destination with the aid of a single small RIS in the presence of a malicious eavesdropper. A new joint RIS location and passive beamforming (J-LPB) optimization scheme is proposed for the sake of maximizing the secrecy rate under the RIS location constraint and the constraint that the modulus of the reflecting coefficient at each RIS's unit is not larger than 1. Specifically, we analyze the optimal location of the RIS, and conclude that the product involving the source-RIS distance and the RIS-destination distance should be minimized. Since the product minimization problem is nonconvex, we then propose a two-tier optimization algorithm for solving it. Based on the near-optimal RIS 3D location obtained, we further formulate the passive beamforming optimization problem, and then propose to apply the Charnes-Cooper transformation along with the sequential rank-one constraint relaxation (SROCR) algorithm to solve it. Our numerical results show that the secrecy rate of the proposed J-LPB optimization scheme is higher than that of the benchmarks. Explicitly, we use the following benchmarks: the near-source-based RIS location and passive beamforming (NSB-LPB) optimization scheme, the near-destination-based RIS location and passive beamforming (NDB-LPB) optimization scheme, and the random RIS location and passive beamforming (R-LPB) optimization scheme. Finally, the benefits of our J-LPB scheme are further increased with the number of RIS units.