基于统计信道状态信息的智能反射面辅助反向散射通信系统鲁棒资源分配算法

徐勇军; 徐娟; 田秦语; 黄崇文

doi:10.11999/JEIT231169

基于统计信道状态信息的智能反射面辅助反向散射通信系统鲁棒资源分配算法

doi: 10.11999/JEIT231169

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
浙江大学信息与电子工程学院杭州 310007

基金项目: 国家自然科学基金(62271094, U23A20279)，重庆市自然科学基金重点项目(CSTB2022NSCQ-LZX0009, CSTB2023NSCQ-LZX0079)，重庆市教委科技重点项目(KJZD-K202200601)，国家重点研发计划(2021YFA1000500) ，重庆市研究生科研创新项目(CYB23241, CYS23450)

详细信息

作者简介:
徐勇军：男，副教授，博士生导师，研究方向为反向散射通信、智能反射面、资源分配等

徐娟：女，硕士生，研究方向为反向散射通信、智能反射面等

田秦语：男，硕士生，研究方向为智能反射面、共生无线电等

黄崇文：男，研究员，研究方向为智能反射面、机器学习、资源分配等

通讯作者:
徐勇军　xuyj@cqupt.edu.cn

1¹⁾反射节点收集的能量主要满足自身电路功耗和信息传输的需求。²⁾假设信道服从准静态衰落，则在同一时间帧的不同时隙的信道可以被视为近似不变的。
中图分类号: TN929.5
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出版历程
- 收稿日期: 2023-10-30
- 修回日期: 2023-12-05
- 网络出版日期: 2023-12-19

Robust Resource Allocation Algorithm for Reconfigurable Intelligent Surface-assisted Backscatter Communication Systems Based on Statistical Channel State Information

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310007, China

Funds: The National Natural Science Foundation of China (62271094, U23A20279), Key Fund of Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009, CSTB2023NSCQ-LZX0079), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The National Key R&D Program of China (20221YFA00500), Graduate Scientific Research Innovation Project of Chongqing (CYB23241, CYS23450)

摘要

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。
- 反向散射通信 /
- 智能反射面 /
- 鲁棒资源分配 /
- 信道不确定性
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.
- Backscatter Communication (BackCom) /
- Reconfigurable Intelligent Surface (RIS) /
- Robust resource allocation /
- Channel uncertainty.

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图 1 RIS辅助的下行多输入单输出反向散射通信系统

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图 2 仿真场景图

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图 3 所提算法的收敛图

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图 4 不同中断速率门限下系统总吞吐量与信道不确定性的关系

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图 5 系统总吞吐量与最小吞吐量阈值的关系

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图 6 不同算法下系统总吞吐量与信道估计误差标准差的关系

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图 7 不同算法下反射单元个数和天线数与系统总吞吐量的关系

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图 8 不同算法下能量中断概率与信道不确定性的关系

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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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表 1 不同算法对比

算法名称	优化目标	有无RIS	鲁棒/非鲁棒
传统非鲁棒算法^[7]	最大化系统吞吐量	有	非鲁棒
无RIS算法^[25]		无	鲁棒
本文所提算法		有	鲁棒

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