智能反射面增强的全双工环境反向散射通信系统波束成形算法

张晓茜; 徐勇军; 吴翠先; 黄崇文

doi:10.11999/JEIT230356

智能反射面增强的全双工环境反向散射通信系统波束成形算法

doi: 10.11999/JEIT230356

张晓茜^{1, 2},
徐勇军^{1, 3, ,},
吴翠先^{1, 2},
黄崇文⁴

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
数智化通信新技术应用研究中心重庆 400065
3.
移动通信技术重庆市重点实验室重庆 400065
4.
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)，重庆市自然科学基金(CSTB2022NSCQ-LZX0009)，重庆市教委科学技术研究项目(KJZD-K202200601)，浙江省信息处理与通信网络重点实验室开放课题(IPCAN-2302)

详细信息

作者简介:
张晓茜：男，博士生，研究方向为反向散射通信、智能反射面等

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

吴翠先：女，正高级工程师，硕士生导师，研究方向为智能反射面、资源分配、共生无线电等

黄崇文：男，教授，博士生导师，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 假设节点的分布情况为稀疏分布，该文探究的模型也同样可以迁移到用户密集分布的情况。² 根据文献[21]，假设RIS 反射多次信号的功率可被忽略，同时考虑其在反射期间无能量损失。
中图分类号: TN929.5
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出版历程
- 收稿日期: 2023-05-04
- 修回日期: 2023-07-12
- 网络出版日期: 2023-07-17
- 刊出日期: 2024-03-27

Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks

ZHANG Xiaoxi^{1, 2},
XU Yongjun^{1, 3
, ,},
WU Cuixian^{1, 2},
HUANG Chongwen⁴

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Research Center of New Digital Intelligent Telecommunication Technology Applications, Chongqing 400065, China
3.
Chongqing Key Laboratory of Mobile Communication Technology, Chongqing 400065, China
4.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310007, China

Funds: The National Natural Science Foundation of China (62271094), Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The Open Project of Zhejiang Provincial Key Laboratory of Information Processing and Communication Networking (IPCAN-2302)

摘要

摘要: 当前传统环境反向散射通信存在双重衰落、障碍物阻挡和网络容量有限的问题。智能反射面(RIS)作为6G的关键候选技术因能够主动改善信号传输质量、提升通信系统传输性能而备受关注。为此，该文将RIS与全双工技术引入到环境反向散射通信系统，研究了考虑硬件损伤与RIS离散相移的RIS增强全双工环境反向散射通信系统波束成形算法。首先，考虑反射节点最小能量收集与服务质量约束、功率站最大发射功率约束和RIS相移约束，建立了总发射功率最小的波束成形优化问题。然后，利用交替优化、半正定松弛、变量替换、半正定规划将原非凸问题转化成可求解的凸优化问题。最后，仿真结果表明，所提算法比传统波束成形方法平均功耗降低了7.8%。
- 环境反向散射通信 /
- 智能反射面 /
- 资源分配 /
- 硬件损伤
Abstract: Current conventional ambient backscatter communication suffers from double fading, obstacle blockage, and limited network capacity. Reconfigurable Intelligent Surface (RIS) as a key candidate technology of 6G has been concerned due to the improvement of signal transmission quality and the enhancement of the transmission performance of communication systems. Based on the above advantages, RIS and full-duplex techniques are introduced into the ambient backscatter communication system, and the beamforming algorithm is designed in an RIS-enhanced full-duplex ambient backscatter communication network with hardware impairments and discrete phase-shift constraints. Firstly, a beamforming optimization problem is formulated to minimize the total transmit power by considering the minimum harvested energy and the quality-of-service constraint of the backscatter nodes, the maximum transmit power constraint of the power station, and the phase shift constraint of the RIS. Then, the original non-convex problem is transformed into a tractable convex optimization problem by using alternating optimization methods, semi-definite relaxation methods, variable substitution, and semi-definite programming. Finally, simulation results show that the proposed algorithm decreases the average power consumption by 7.8% compared to the conventional beamforming method.
- Ambient backscatter communication /
- Reconfigurable Intelligent Surface (RIS) /
- Resource allocation /
- Hardware impairments

HTML全文

图 1 RIS增强的全双工环境反向散射通信系统模型

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图 2 不同算法下HAP发射功率与反射节点到HAP距离$D$的关系

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图 3 不同算法下HAP发射功率与硬件损伤因子$\kappa $的关系

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图 4 不同算法下HAP发射功率与最小信干噪比$\gamma _k^{\min }$的关系

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图 5 不同算法下HAP发射功率与反射单元数$M$的关系

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图 6 不同算法下HAP发射功率与最小信干噪比$\gamma _k^{\min }$的关系

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图 7 不同算法下HAP发射功率与反射节点数量$K$的关系

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算法1　基于迭代的发射功率最小化波束成形算法
初始化系统参数：$M$, $K$, $ {N}_{\mathrm{t}} $, ${N_{ {\rm{r} } } }$, $T$, ${{\boldsymbol{F}}_{\rm d} }$, ${{\boldsymbol{F}}_{\rm u} }$, ${{\boldsymbol{g}}_{ { {\rm{d} } } ,k} }$, ${{\boldsymbol{g}}_{ { {\rm{u} } } ,k} }$, ${{\boldsymbol{h}}_{ { {\rm{d} } } ,k} }$, 　${{\boldsymbol{h}}_{ { {\rm{u} } } ,k} }$, ${ { {{\boldsymbol{\varTheta}} } }_{\rm u} }$, ${ { {{\boldsymbol{\varPhi}} } }_{\rm u} }$, ${ { {{\boldsymbol{\varTheta}} } }_{\rm d} }$, ${ { {{\boldsymbol{\varPhi}} } }_{\rm d} }$, $ {\kappa _{\rm d} } $, ${\kappa _{ {{\rm{u}}} ,k} }$, $b$, ${\mu _k}$, $ {\beta _k} $, $\bar \sigma _{ {{\rm{B}}} ,k}^2$, $\sigma _k^2$, $ \gamma _k^{\min } $, 　$ {P^{\max }} $, $ E_k^{\min } $, ${\boldsymbol{v}}_{\rm u} ^{(l)}$, ${\boldsymbol{v}}_{\rm d} ^{(l)}$, $ {\boldsymbol{w}}_k^{(l)} $；设置收敛精度$\varepsilon = {10^{ - 5} }$，最大　迭代次数$ {L_{\max }} $，初始化$l = 1$，令$\mathcal{Q} = \displaystyle\sum\nolimits_{k = 1}^K { {\rm{Tr} }({ {\boldsymbol{w} }_k}{\boldsymbol{w} }_k^{\rm{H} })}$；
(1) While $\left\| { {\mathcal{Q}^{(l+1)} } - {\mathcal{Q}^{(l )} } } \right\| \ge \varepsilon$或$l \le {L_{\max } }$ do
(2) 　Repeat
(3) 　　固定$\{ {\boldsymbol{v}}_{\rm u} ^{(l)},{\boldsymbol{v}}_{\rm d} ^{(l)}\} $，求解式(11)得到$ {\boldsymbol{W}}_k^* $，进而得到$ {\boldsymbol{w}}_k^* $ 　　　　并更新$ {\boldsymbol{w}}_k^{(l + 1)} $；
(4) 　Until 收敛；
(5) 　Repeat
(6) 　　固定$ \{ {\boldsymbol{w}}_k^{(l + 1)},{\boldsymbol{v}}_{\rm d} ^{(l)}\} $，求解式(17)得到${\boldsymbol{U}}_{\rm u} ^$，进而得到${\boldsymbol{v}}_{\rm u} ^$ 　　　　并更新${\boldsymbol{v}}_{\rm u} ^{(l + 1)}$；
(7) 　Until 收敛；
(8) 　Repeat
(9) 　　固定$ {\boldsymbol{w}}_k^{(l + 1)} $和${\boldsymbol{v}}_{\rm u} ^{(l + 1)}$，求解式(22)得到${\boldsymbol{U}}_{\rm d} ^$，进而得到　　　　 ${\boldsymbol{v}}_{\rm d} ^$并更新${\boldsymbol{v}}_{\rm d} ^{(l + 1)}$；
(10) Until 收敛；
(11) 并令$l = l + 1$将得到的$ \{ {\boldsymbol{w}}_k^{(l+1)},{\boldsymbol{v}}_{\rm u} ^{(l+1)},{\boldsymbol{v}}_{\rm d} ^{(l+1)}\} $代入计算　　　 $ {\mathcal{Q}^{(l+1)}} $；
(12) End while
(13) 得到$\{ {\boldsymbol{w} }_k^* = {\boldsymbol{w} }_k^{(l)},k \in \mathcal{K},{\boldsymbol{v}}_{\rm u} ^* = {\boldsymbol{v} }_{\rm u} ^{(l)},{\boldsymbol{v}}_{\rm d} ^* = {\boldsymbol{v} }_{\rm d} ^{(l)}\}$。

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表 1 不同算法对比

算法名称	优化目标	模式	RIS相移	硬件状态
半双工算法^[27]	最小化发射功率	半双工	/	理想
全双工算法^[28]		全双工	/	非理想
RIS辅助算法^[29]		半双工	连续相移	理想
所提算法		全双工	离散相移	非理想

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参考文献(29)

[1]	MA Dong, LAN Guohao, HASSAN M, et al. Sensing, computing, and communications for energy harvesting IoTs: A survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(2): 1222–1250. doi: 10.1109/COMST.2019.2962526.
[2]	LI Xingwang, WANG Qunshu, ZENG Ming, et al. Physical-layer authentication for ambient backscatter-aided NOMA symbiotic systems[J]. IEEE Transactions on Communications, 2023, 71(4): 2288–2303. doi: 10.1109/TCOMM.2023.3245659.
[3]	XU Yongjun, GUI Guan, GACANIN H, et al. A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2): 668–695. doi: 10.1109/COMST.2021.3059896.
[4]	LI Xingwang, ZHENG Yike, KHAN W U, et al. Physical layer security of cognitive ambient backscatter communications for green Internet-of-Things[J]. IEEE Transactions on Green Communications and Networking, 2021, 5(3): 1066–1076. doi: 10.1109/TGCN.2021.3062060.
[5]	XU Yongjun, XIE Hao, WU Qingqing, et al. Robust max-min energy efficiency for RIS-aided HetNets with distortion noises[J]. IEEE Transactions on Communications, 2022, 70(2): 1457–1471. doi: 10.1109/TCOMM.2022.3141798.
[6]	徐勇军, 刘子腱, 李国权, 等. 基于NOMA的无线携能D2D通信鲁棒能效优化算法[J]. 电子与信息学报, 2021, 43(5): 1289–1297. doi: 10.11999/JEIT200175. XU Yongjun, LIU Zijian, LI Guoquan, et al. Robust energy efficiency optimization algorithm for NOMA-based D2D communication with simultaneous wireless information and power transfer[J]. Journal of Electronics &Information Technology, 2021, 43(5): 1289–1297. doi: 10.11999/JEIT200175.
[7]	张晓茜, 徐勇军. 面向零功耗物联网的反向散射通信综述[J]. 通信学报, 2022, 43(11): 199–212. doi: 10.11959/j.issn.1000-436x.2022199. ZHANG Xiaoxi and XU Yongjun. Survey on backscatter communication for zero-power IoT[J]. Journal on Communications, 2022, 43(11): 199–212. doi: 10.11959/j.issn.1000-436x.2022199.
[8]	CHEN Yunfei. Performance of ambient backscatter systems using reconfigurable intelligent surface[J]. IEEE Communications Letters, 2021, 25(8): 2536–2539. doi: 10.1109/LCOMM.2021.3083110.
[9]	MA Hui, ZHANG Haijun, ZHANG Ning, et al. Reconfigurable intelligent surface with energy harvesting assisted cooperative ambient backscatter communications[J]. IEEE Wireless Communications Letters, 2022, 11(6): 1283–1287. doi: 10.1109/LWC.2022.3164257.
[10]	GALAPPATHTHIGE D L, REZAEI F, TELLAMBURA C, et al. RIS-empowered ambient backscatter communication systems[J]. IEEE Wireless Communications Letters, 2023, 12(1): 173–177. doi: 10.1109/LWC.2022.3220158.
[11]	CHEN Hao, YANG Gang, and LIANG Yingchang. Joint active and passive beamforming for reconfigurable intelligent surface enhanced symbiotic radio system[J]. IEEE Wireless Communications Letters, 2021, 10(5): 1056–1060. doi: 10.1109/LWC.2021.3056874.
[12]	HU Jinlin, LIANG Yingchang, and PEI Yiyang. Reconfigurable intelligent surface enhanced multi-user MISO symbiotic radio system[J]. IEEE Transactions on Communications, 2021, 69(4): 2359–2371. doi: 10.1109/TCOMM.2020.3047444.
[13]	ZUO Jiakuo, LIU Yuanwei, YANG Liang, et al. Reconfigurable intelligent surface enhanced NOMA assisted backscatter communication system[J]. IEEE Transactions on Vehicular Technology, 2021, 70(7): 7261–7266. doi: 10.1109/TVT.2021.3087582.
[14]	LIU Qiang, FU Meixia, LI Wei, et al. RIS-assisted ambient backscatter communication for SAGIN IoT[J]. IEEE Internet of Things Journal, 2023, 10(11): 9375–9384. doi: 10.1109/JIOT.2022.3224587.
[15]	RAMEZANI P and JAMALIPOUR A. Backscatter-assisted wireless powered communication networks empowered by intelligent reflecting surface[J]. IEEE Transactions on Vehicular Technology, 2021, 70(11): 11908–11922. doi: 10.1109/TVT.2021.3116708.
[16]	MAO Sun, YANG Kun, HU Jie, et al. Intelligent reflecting surface-aided wireless powered hybrid backscatter-active communication networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(1): 1383–1388. doi: 10.1109/TVT.2022.3205950.
[17]	WANG Jinming, XU Sai, HAN Shuai, et al. Multicast secrecy rate maximization for reconfigurable intelligent surface backscatter communication[J]. IEEE Communications Letters, 2022, 26(12): 2855–2859. doi: 10.1109/LCOMM.2022.3208018.
[18]	LI Xingwang, ZHENG Yike, ZENG Ming, et al. Enhancing secrecy performance for STAR-RIS NOMA networks[J]. IEEE Transactions on Vehicular Technology, 2023, 72(2): 2684–2688. doi: 10.1109/TVT.2022.3213334.
[19]	WANG Xinyi, FEI Zesong, and WU Qingqing. Integrated sensing and communication for RIS assisted backscatter systems[J]. IEEE Internet of Things Journal, 2023.
[20]	SHAIKH M H N, BOHARA V A, SRIVASTAVA A, et al. A downlink RIS-aided NOMA system with hardware impairments: Performance characterization and analysis[J]. IEEE Open Journal of Signal Processing, 2022, 3: 288–305. doi: 10.1109/OJSP.2022.3194416.
[21]	YE Yinghui, SHI Liqin, CHU Xiaoli, et al. Mutualistic cooperative ambient backscatter communications under hardware impairments[J]. IEEE Transactions on Communications, 2022, 70(11): 7656–7668. doi: 10.1109/TCOMM.2022.3201119.
[22]	XING Zhe, WANG Rui, WU Jun, et al. Achievable rate analysis and phase shift optimization on intelligent reflecting surface with hardware impairments[J]. IEEE Transactions on Wireless Communications, 2021, 20(9): 5514–5530. doi: 10.1109/TWC.2021.3068225.
[23]	XU Yongjun, XIE Hao, LI Dong, et al. Energy-efficient beamforming for heterogeneous industrial IoT networks with phase and distortion noises[J]. IEEE Transactions on Industrial Informatics, 2022, 18(11): 7423–7434. doi: 10.1109/TII.2022.3158612.
[24]	ZHOU Hu, KANG Xin, LIANG Yingchang, et al. Cooperative beamforming for reconfigurable intelligent surface-assisted symbiotic radios[J]. IEEE Transactions on Vehicular Technology, 2022, 71(11): 11677–11692. doi: 10.1109/TVT.2022.3190515.
[25]	ZHOU Gui, PAN Cunhua, REN Hong, et al. A framework of robust transmission design for IRS-aided MISO communications with imperfect cascaded channels[J]. IEEE Transactions on Signal Processing, 2020, 68: 5092–5106. doi: 10.1109/TSP.2020.3019666.
[26]	BEN-TAL A and NEMIROVSKIAEI A S. Lectures on modern convex optimization: Analysis, algorithms, and engineering applications[M]. Philadelphia, USA: SIAM, 2001: 436-437. doi: 10.1137/1.9780898718829.
[27]	ZOU Yuze, XU Jing, GONG Shimin, et al. Backscatter-aided hybrid data offloading for wireless powered edge sensor networks[C]. 2019 IEEE Global Communications Conference, Waikoloa, USA, 2019: 1–6.
[28]	LONG Ruizhe, GUO Huayan, and LIANG Yingchang. Symbiotic radio with full-duplex backscatter devices[C]. 2019 IEEE International Conference on Communications, Shanghai, China, 2019: 1–6.
[29]	JIA Xiaolun, ZHOU Xiangyun, NIYATO D, et al. Intelligent reflecting surface-assisted bistatic backscatter networks: Joint beamforming and reflection design[J]. IEEE Transactions on Green Communications and Networking, 2022, 6(2): 799–814. doi: 10.1109/TGCN.2021.3127190.