Robust Resource Allocation Algorithm for Reconfigurable Intelligent Surface-assisted Backscatter Communication Systems Based on Statistical Channel State Information
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摘要: 为解决传统反向散射通信(BackCom)系统存在通信距离短、系统吞吐量较低和克服信道不确定性能力差的问题，该文提出一种基于统计信道状态信息(CSI)的智能反射面(RIS)辅助反向散射通信系统鲁棒资源分配算法。考虑功率站最大发射功率约束、反射节点的能量中断约束和吞吐量中断约束、反射系数约束、RIS相移约束和信息传输时间约束，建立了系统加权和吞吐量最大化的鲁棒资源分配模型；利用伯恩斯坦不等式、交替优化和半正定松弛方法，将原非凸问题转换成凸优化问题求解，并提出一种基于迭代的鲁棒吞吐量最大化算法。仿真结果表明，与传统非鲁棒资源分配算法和无RIS资源分配算法相比，所提算法具有更强的鲁棒性和更高的吞吐量。Abstract: In order to solve the problems of short-distance communication, lower system throughput and the effects of channel uncertainties in traditional Backscatter Communication (BackCom) systems, a robust resource allocation algorithm for a Reconfigurable Intelligent Surface (RIS)-assisted backscatter communication system with statistical Channel State Information (CSI) is proposed in this paper. A system weighting and sum throughput-maximization robust resource allocation model is formulated by considering the maximum transmit power constraint of the power station, the energy outage constraint and throughput outage constraint of backscatter nodes, the reflection coefficient constraint, the phase shift constraint of the RIS and the information transmission time constraint; Then, the original non-convex problem is transformed into a convex optimization problem by using the methods of Bernstein-type inequality, the alternating optimization, and the semi-definite relaxation technique. An iteration-based robust throughput maximization algorithm is designed. Simulation results show that the proposed algorithm had stronger robustness and higher throughput compared it with the traditional non-robust resource allocation algorithm and the resource allocation algorithm without RIS.
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算法1 基于迭代的鲁棒吞吐量最大化算法 初始化系统参数: $ M,K,L,T,P_k^C,R_k^{\min},{\bar g_k},{{\boldsymbol{g}}_{l,k}},{\bar {\boldsymbol{f}}_k},{\bar {\boldsymbol{h}}_k},\sigma _{{H},K}^2 $,
$\sigma _{h,k}^2,{P_{\max}},{\delta _k} $, $ {\upsilon _k},{\tilde {\boldsymbol{\varTheta}} ^{(l - 1)}},t_k^{(l - 1)},\alpha _k^{(l - 1)} $，收敛精度$ \varepsilon , $设置内
层最大迭代次数${L_{\max}},$初始化迭代次数$ l = 0, $令
$ {R_{{\mathrm{sum}}}} = \sum\nolimits_{k = 1}^K {{\psi _k}{R_k}} {\text{ }} $(1) While $ |R_{{\mathrm{sum}}}^{(l)} - R_{{\mathrm{sum}}}^{(l - 1)}| \ge \varepsilon $或$l \le {L_{\max}},$do (2) 设置迭代次数$ l = l + 1 $ (3) Repeat (4) 固定$ \{ {\tilde {\boldsymbol{\varTheta}} ^{(l - 1)}},t_k^{(l - 1)},\alpha _k^{(l - 1)}\} $求解式(20)得到$ {{\boldsymbol{W}}^{(l)}} $，进
而得到$ {{\boldsymbol{w}}_k}^* $并更新$ {\boldsymbol{w}}_k^{(l)}; $(5) Until收敛 (6) Repeat (7) 固定$ \{ {\boldsymbol{w}}_k^{(l)},\alpha _k^{(l - 1)}\} $求解式(23)得到$ \{ {\tilde {\boldsymbol{\varTheta }}^{(l)}},t_k^{(l)}\} $进而得
到$ \theta _l^ * $和$ t_k^ * $并更新$ \{ \theta _l^{(l)},t_k^{(l)}\} $(8) Until收敛 (9) Repeat (10) 固定$ \{ {\boldsymbol{w}}_k^{(l)},\theta _l^{(l)},t_k^{(l)}\} $求解式(25)得到$ \alpha _k^ * $并更新$ \alpha _k^{(l)} $ (11) Until收敛 (12) 将$ \{ {\boldsymbol{w}}_k^{(l)},\theta _l^{(l)},t_k^{(l)},\alpha _k^{(l)}\} $带入更新$ {R_{{\mathrm{sum}}}} $ (13) End while (14) 得到最优的$ \{ {{\boldsymbol{w}}_k}^* = {\boldsymbol{w}}_k^{(l)},\theta _l^ * = \theta _l^{(l)},t_k^ * = t_k^{(l)},\alpha _k^ * = \alpha _k^{(l)}\} $ 
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