Robust Energy Efficiency Maximization Algorithm for Intelligent Reflecting Surface-aided Wireless Powered-communication Networks
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摘要: 为了解决能量收集效率易受到障碍物阻挡和信道不确定性影响的问题，该文提出一种基于智能反射面(IRS)辅助的无线供电通信网络鲁棒能效(EE)最大化算法。首先，考虑最小收集能量、IRS相移、最小吞吐量等约束，基于有界信道不确定性，建立一个联合优化能量波束、相移、传输时间的多变量耦合非线性资源分配模型。然后，利用最坏准则、变量替换和S-Procedure等方法，将原非凸问题转换为确定性凸优化问题，同时，提出一种基于迭代的鲁棒能效最大化算法进行求解。仿真结果表明，与现有算法比较，该文算法具有较好的能效和鲁棒性。Abstract: To resolve the effect of channel uncertainties and low energy transfer efficiency caused by obstacles, a robust Energy Efficiency (EE) maximization algorithm for an Intelligent Reflecting Surface (IRS)-assisted Wireless-Powered Communication Network (WOCN) is proposed. Firstly, considering the constraint of the minimum energy harvesting, the constraint of the phase-shift, and the constraint of the minimum throughput, a multi-variable coupling nonlinear resource allocation model that jointly optimizing the energy beamforming, the phase shifts, and the transmission time is established based on the bounded channel uncertainties. Then, the original non-convex problem is transformed into a deterministic convex optimization problem by using the worst-case approach, the variable substitution and S-Procedure methods. At the same time, a robust EE maximization algorithm based on iteration is proposed to solve the problem. The simulation results show that the proposed algorithm has better EE and robustness by comparing it with the existing algorithms.
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表 1 基于迭代的鲁棒能效最大化算法
初始化系统参数：$ M $, $ N $, $ K $, $ T $, $ P_{\text{B}}^{\text{C}} $, $ P_{\text{e}}^{\text{C}} $, $ p_k^{\text{C}} $, $ P_{\text{D}}^{\text{C}} $, $ {\bar {\boldsymbol{G}}_k} $, $ {\bar g_k} $, $ {\omega _k} $, $ {\sigma _k} $, $ R_k^{\min } $, $ \eta $, $ {P^{\max }} $, $ {q^{(0)}} $, $ {{\boldsymbol{v}}^{(0)}} $；设置收敛精度$ \varepsilon \ge 0 $，最大迭代次数$ {L_{\max }} $，
初始化$ l \ge 0 $；(1) While $ \left| {{q^{(l)}} - {q^{(l - 1)}}} \right| \ge \varepsilon $或$l \le {L_{\max } }$ do (2) 设置迭代次数$ l = l + 1 $； (3) 固定$ {{\boldsymbol{v}}^{(l - 1)}} $，根据式(18)计算$ \left\{ {{{\boldsymbol{W}}^{(l)}},t_0^{(l)},t_k^{(l)},p_k^{(l)}} \right\} $； (4) 固定$ \left\{ {{{\boldsymbol{W}}^{(l)}},t_0^{(l)},t_k^{(l)},p_k^{(l)}} \right\} $，根据式(20)计算$ {{\boldsymbol{V}}^{(l)}} $； (5) 特征值分解$ {{\boldsymbol{V}}^{(l)}} = {\boldsymbol{U}}{\boldsymbol{\varLambda}} {\boldsymbol{U}} $, $ {{\boldsymbol{v}}^{(l)}} = {\boldsymbol{U}}{{\boldsymbol{\varLambda}} ^{(1/2)}}{\boldsymbol{r}} $；
(6) 更新能效$ {q^{(l)}} = \frac{{\displaystyle\sum\limits_{k = 1}^K {{t_k}{{\log }_2}} \left( {1 + \frac{{{p_k}{{\tilde g}_k}}}{{{\delta ^2}}}} \right)}}{{{t_0}(P_{\text{B}}^{\text{C}} + NP_{\text{e}}^{\text{C}}) + \displaystyle\sum\limits_{k = 1}^K {({t_0} + {t_k})} p_k^{\text{C}} + {t_0}{\text{Tr(}}{\boldsymbol{W}}{\text{)}} - \displaystyle\sum\limits_{k = 1}^K {{\chi _k}} + \displaystyle\sum\limits_{k = 1}^K {{t_k}{p_k}} + \displaystyle\sum\limits_{k = 1}^K {{t_k}P_{\text{D}}^{\text{C}}} }} $;(7) End While 
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