Robust Secure Resource Allocation Algorithm for Intelligent Reflecting Surface-assisted Multi-antenna Communication Systems
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摘要: 为了解决蜂窝通信系统中因窃听者、障碍物阻挡和信道不确定性导致安全性低和传输质量差的问题，该文提出一种智能反射面(IRS)辅助的多天线通信系统鲁棒安全资源分配算法。首先，考虑合法用户的安全速率约束、最大发射功率约束和IRS相移约束，基于有界信道不确定性，建立了一个联合优化基站主动波束、IRS被动波束的鲁棒资源分配问题。然后，利用S-程序、连续凸近似、交替优化和罚函数等方法对含参数摄动的原非凸问题进行转换，得到可直接求解的确定性凸优化问题。最后，提出一种基于迭代的鲁棒能效最大化算法。仿真结果表明，该文算法具有较好的能效和较强的鲁棒性。Abstract: To solve the problem of low security and poor transmission quality in cellular communication systems caused by eavesdroppers, obstacles and channel uncertainties, a robust secure resource allocation algorithm for Intelligent Reflecting Surface (IRS)-assisted multi-antenna communication systems is proposed. Firstly, a robust resource allocation problem with bounded channel uncertainties is formulated by jointly optimizing the active beam of the base station, the passive beam of the IRS, meanwhile the secure rate constraint of legitimate users, the maximum transmit power constraint and the phase shift constraint of the IRS are considered. Then, the original non-convex problem with parametric perturbation is transformed using S-procedure, successive convex approximation, alternating optimization and penalty function to obtain a deterministic convex optimization problem that can be solved directly. Finally, an iteration-based robust energy efficiency maximization algorithm is proposed. Simulation results show that the proposed algorithm has good energy efficiency and strong robustness.
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算法1 基于迭代的鲁棒能效最大化算法 初始化系统参数：$ N,L,M,K,\mu ,{P^{\text{c}}},{P^{\max }},R_k^{\min },{{\boldsymbol{\bar H}}_k},{{\boldsymbol{\bar h}}_k},{{\boldsymbol{\bar G}}_m},{{\boldsymbol{\bar g}}_m},\xi _k^{\text{h}},\xi _k^{\text{H}},\xi _m^{\text{g}},\xi _m^{\text{G}},{\tau ^{(0)}},{\kern 1pt} {\rho ^{(0)}},r_k^{(0)},\beta _k^{(0),{\text{U}}},\phi _k^{(0)},\beta _{k,m}^{(0),{\text{E}}},{{\boldsymbol{w}}^{(0)}},{\kern 1pt} {{\boldsymbol{v}}^{(0)}},{\kern 1pt} {\lambda _{\max }} $，
能效${\varepsilon ^{(0)}}$；设置收敛精度$ \psi \ge 0 $,$ \vartheta \ge 1 $，最大迭代次数${T_{\max }}$，初始化$t \ge 1$，迭代停止条件${\vartheta _1},{\vartheta _2}$；(1) 重复 (2) 通过给的$\{ {{\boldsymbol{v}}^{(t - 1)}},{\tau ^{(t - 1)}},{\rho ^{(t - 1)}},r_k^{(t - 1)},\beta _k^{(t - 1),{\text{U}}},\phi _k^{(t - 1)},\beta _{k,m}^{(t - 1),{\text{E}}}\} $，求解问题式(20)获得$\{ {{\boldsymbol{w}}^{(t)}},{\tau ^{(t)}},{\rho ^{(t)}},r_k^{(t)},\beta _k^{(t),{\text{U}}},\phi _k^{(t)},\beta _{k,m}^{(t),{\text{E}}}\} $； (3) 重复 (4) 通过$\{ {{\boldsymbol{w}}^{(t)}},{\tau ^{(t)}},{\rho ^{(t)}},r_k^{(t)},\beta _k^{(t),{\text{U}}},\phi _k^{(t)},\beta _{k,m}^{(t),{\text{E}}}\} $，求解问题式(22)获得$\{ {{\boldsymbol{v}}^{(t + 1)}},{\tau ^{(t + 1)}},{\rho ^{(t + 1)}},r_k^{(t + 1)},\beta _k^{(t + 1),{\text{U}}},\phi _k^{(t + 1)},\beta _{k,m}^{(t + 1),{\text{E}}}\} $； (5) 更新${\lambda ^{(t)}} = \min \{ \vartheta {\lambda ^{(t - 1)}},{\lambda _{\max }}\} $, ${\tau ^{(t)}} = {\tau ^{(t + 1)}}$, ${\rho ^{(t)}} = {\rho ^{(t + 1)}}$, $r_k^{(t)} = r_k^{(t + 1)}$, $\beta _k^{(t),{\text{U}}} = \beta _k^{(t + 1),{\text{U}}}$； (6) 直到$\phi _k^{(t)} = \phi _k^{(t + 1)}$, $\beta _{k,m}^{(t),{\text{E}}} = \beta _{k,m}^{(t + 1),{\text{E}}}$ (7) 更新$ ||{\boldsymbol{z}}|{|_1} \le {\vartheta _1} $和$||{{\boldsymbol{v}}^{(t)}} - {{\boldsymbol{v}}^{(t - 1)}}|{|_1} \le {\vartheta _2}$； (8) 更新${\varepsilon ^{(t + 1)}} = {\varepsilon ^{(t)}}$, $t = t + 1$； (9) 直到$\left| {{\varepsilon ^{(t)}} - {\varepsilon ^{(t - 1)}}} \right| \ge \psi $或$t \le {T_{\max }}$ 
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