Research on Physical Layer Security Communication Based on Distributed Intelligent Reflective Surface
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摘要: 智能反射面(IRS)能够实时调整无线传输环境提高通信效率，在后5G和6G研究中得到广泛关注。该文研究分布式IRSs安全速率最大化问题：考虑功率和恒模约束以及IRS链路之间的相关性，以最大化安全传输速率为目标，构建基站波束成形和IRSs相移参数联合优化问题。采用分式规划和流形优化算法求解构建的非凸优化方程。仿真结果表明，相较于传统算法，该文算法具有较高处理效率有效提高系统安全性，也进一步表明分布式部署IRS比集中部署安全性能更优。Abstract: Intelligent Reflecting Surface (IRS) has been widely used in the after 5G and 6G to increase the communication efficiency by adjusting the wireless transmission channels in real-time. In this paper, the use of distributed IRSs to maximize the secrecy rate is investigated. Considering the constraints for the power, constant-mode and the correlation between the IRS links, the optimization problem of the secrecy rate maximization based on the active beamforming of the base station and IRSs’ phase shifts is formulated. An efficient algorithm is proposed to solve the non-convex optimization problem by using the fractional programming and manifold optimization methods. Finally, simulation results confirm, compared with the conservative methods, the proposed algorithm can significantly improve the secrecy communication rate. Furthermore, the results unveil that the distributed IRS scheme achieves a significant secure performance improvement over the centralized IRS.
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算法1 IRSs相移优化算法 (1) 设置迭代次数$r = 0{\boldsymbol{}} $； (2) 重复执行如下操作：
根据式(11)计算$y_1^{*r} = \dfrac{ {{\boldsymbol{v}}_{}^{\text{H} }{\boldsymbol{a}}} }{ {1 + { {\left| {{\boldsymbol{v}}_{}^{\text{H} }{\boldsymbol{b}}} \right|}^2} } },y_2^{*r} =$$\dfrac{1}{{1 + {{\left| {v_{}^{\text{H}}b} \right|}^2}}} $；采用MO算法获取${{\boldsymbol{v}}^{r + 1} }$； 更新$r = r + 1,{\boldsymbol{v}} = {v^{r + 1} }$； 直至优化问题P3收敛； (3) 获取当前相移矩阵${{\boldsymbol{\varTheta}} }_{l}=\text{diag}({({{\boldsymbol{v}}}_{l}^{r})}^{*}),\forall L$。 
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算法2 交替迭代优化算法 (1) 设置迭代次数$ i = 0 $, ${ {\boldsymbol{w} }^{\left( 0 \right)} } = \dfrac{ { {{\boldsymbol{h}}_{r,l} } } }{ {\left\| { {{\boldsymbol{h}}_{r,l} } } \right\|} }$,
${\boldsymbol{\varTheta}} _l^{(0)} = {\text{diag} }({1_1}, \cdots ,{1_{ {M_l} } })$, $R_s^{(0)}({\boldsymbol{w}},{{\boldsymbol{\varTheta}} _l})$, $ \varepsilon $；(2) 重复执行如下操作： $ i = i + 1 $； 给定${\boldsymbol{\varTheta } }_l^{(i - 1)}$，根据式(7)更新${{\boldsymbol{w}}^{(i)} }{\text{ = } }$$\sqrt { {P_{\max } } } {\lambda _{\max } }(\overline {\boldsymbol{X}} _e^{ - 1}{\overline {\boldsymbol{X}} _u})$； 给定${{\boldsymbol{w}}^{\left( i \right)} }$，根据表1更新${\boldsymbol{\varTheta}} _l^{(i)}$； 计算${\boldsymbol{R}}_s^{(i)}({{\boldsymbol{w}}^i},{\boldsymbol{\varTheta}} _l^i)$；
直至$\left| {\dfrac{ {R_s^{(i)} - R_s^{(i - 1)} } }{ {R_s^{(i)} } } } \right| \le \varepsilon$；(3) 获取${ {\boldsymbol{\varTheta} } }_{l}^{*}={ {\boldsymbol{\varTheta} } }_{l}^{(i)},\forall L,{{\boldsymbol{w}}}^{*}={{\boldsymbol{w}}}^{\left(i\right)}$。
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