Robust Resource Allocation Algorithm for RIS-Assisted MISO Systems with Eavesdroppers
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摘要: 针对信道不确定性影响、用户信息泄露和能效提升等问题，该文提出一种基于不完美信道状态信息的可重构智能反射面(RIS)多输入单输出系统鲁棒资源分配算法。首先，考虑能量收集最小接收功率约束、合法用户最小保密速率约束、基站最大发射功率约束及RIS相移约束，基于有界信道不确定性，建立一个联合优化基站主动波束、能量波束、RIS相移矩阵的多变量耦合非线性资源分配问题。然后，利用Dinkelbach，S-procedure和交替优化方法，将原非凸问题转换成确定性凸优化问题，并提出一种基于连续凸近似的交替优化算法。仿真结果表明，与传统非鲁棒算法对比，所提算法具有较低的中断概率。Abstract: To resolve the problems of the effect of channel uncertainties, information leakage of users and energy-efficient improvement, a robust resource allocation algorithm for Reconfigurable Intelligent Surface (RIS)-assisted multiple-input single-output systems with eavesdroppers and imperfect channel state information is proposed. Firstly, considering the constraints of the minimum received power of energy-harvesting devices, the minimum secure rate of each legitimate user, the maximum transmit power of the base station and the phase-shift matrix of RIS, a multi-variable coupling and nonlinear resource optimization problem is formulated by jointly optimizing the information beamforming, energy beamforming and the phase shifts under bounded channel uncertainties. Then, the original non-convex problem is transformed into a deterministic convex one by using Dinkelbach’s method, S-Procedure and the alternating optimization method. An alternating optimization algorithm based on successive convex approximation is proposed to solve the problem. Simulation results show that the proposed algorithm has lower outage probabilities by comparing it with the traditional non-robust algorithms.
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表 1 基于连续凸近似的交替优化算法
(1)初始化参数：相移向量$ {{\boldsymbol{q}}^{(0)}} $,波束矩阵$ \{ {\boldsymbol{W}}_k^{(0)}\} ,{\boldsymbol{V}}_r^{(0)} $,初始迭代次数$ i = 1 $,最大迭代次数$ {i_{\max }} $,初始能效$ {\eta ^{(0)}} = 0 $,收敛精度$ \varepsilon $; (2)重复 (a)通过给定的相移$ {{\boldsymbol{q}}^{(i - 1)}} $和$ {\eta ^{(i - 1)}} $求解问题式(32)获得$ \{ {\boldsymbol{W}}_k^{(i)}\} ,{\boldsymbol{V}}_r^{(i)} $； (b)更新$ {\boldsymbol{W}}_k^{(i + 1)}{\text{ = }}{\boldsymbol{W}}_k^{(i)} $，$ {\boldsymbol{V}}_r^{(i + 1)}{\text{ = }}{\boldsymbol{V}}_r^{(i)} $，根据$ {{\boldsymbol{Q}}^{(i - 1)}} $求解问题式(45)获得$ {{\boldsymbol{Q}}^{(i)}} $，由$ {{\boldsymbol{Q}}^{(i)}} = {{\boldsymbol{\bar q}}^{(i)}}\;{({{\boldsymbol{\bar q}}^{(i)}})^{\text{H}}} $和$ {{\boldsymbol{q}}^{(i)}}\; = {[{{\boldsymbol{\bar q}}^{(i)}}]_{(\;1:N)}} $获得$ {{\boldsymbol{q}}^{(i)}} $； (c)更新$ {{\boldsymbol{Q}}^{(i + 1)}}{\text{ = }}{{\boldsymbol{Q}}^{(i)}} $，$ {\eta ^{(i + 1)}} = {\eta ^{(i)}} $，$ i = i + 1 $；
(3)直到 $\left| {\dfrac{ { {\eta ^{(i + 1)} } - {\eta ^{(i)} } } }{ { {\eta ^{(i)} } } }} \right| \le \varepsilon$，获得$ \{ {\boldsymbol{W}}_k^*\} ,{\boldsymbol{V}}_r^*,{{\boldsymbol{q}}^*} $。
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