基于不完美CSI的认知反向散射通信吞吐量最大化算法

徐勇军; 姜思巧; 王公仆; 杨刚; 李东; 黄东

doi:10.11999/JEIT221483

基于不完美CSI的认知反向散射通信吞吐量最大化算法

doi: 10.11999/JEIT221483

徐勇军^{1, 2, ,},
姜思巧¹,
王公仆³,
杨刚⁴,
李东⁵,
黄东⁶

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆金美通信有限责任公司重庆 400030
3.
北京交通大学计算机与信息技术学院北京 100044
4.
电子科技大学通信抗干扰技术国家级重点实验室成都 611731
5.
澳门科技大学计算机科学与工程学院澳门 999078
6.
贵州大学现代制造技术教育部重点实验室贵阳 550025

基金项目: 国家自然科学基金(62271094, U21A20448)，重庆市教委科学技术研究项目(KJZD-K202200601)，中国博士后科学基金(2022MD723725)，重庆市博士后研究项目(2021XM3082, 2021XSJL004)

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、鲁棒资源分配

姜思巧：女，硕士生，研究方向为反向散射、鲁棒资源分配

王公仆：男，教授，博士生导师，研究方向为环境反向散射通信、信号处理以及人工智能等

杨刚：男，研究员，博士生导师，研究方向为智能反射/共生无线通信、智能可重构无线通信等

李东：男，副教授，研究方向为反向散射通信、智能反射面辅助通信和通信资源受限的联邦学习

黄东：男，教授，博士生导师，研究方向为无线通信、5G和6G等

通讯作者:
徐勇军　xuyj@cqupt.edu.cn

中图分类号: TN926
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-28
- 修回日期: 2023-04-12
- 网络出版日期: 2023-04-17
- 刊出日期: 2023-07-10

Throughput Maximization Algorithm for Cognitive Backscatter Communication with Imperfect CSI

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Jinmei Communication Co. LTD., Chongqing 400030, China
3.
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
4.
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
5.
Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
6.
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Gui Zhou University, Guiyang 550025, China

Funds: The National Natural Science Foundation of China (62271094, U21A20448), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The China Postdoctoral Science Foundation (2022MD723725), The Chongqing Postdoctoral Research Project (2021XM3082, 2021XSJL004)

摘要

摘要: 为了提高频谱传输效率和抑制信道不确定性影响，该文提出一种基于不完美信道状态信息的认知反向散射通信吞吐量最大化算法。首先，考虑主基站最大发射功率、传输时间、用户服务质量、有界信道不确定性等约束，建立了联合优化主基站波束、传输时间、反射系数的多变量耦合的非线性鲁棒吞吐量最大化模型。其次，利用最坏准则、S-Procedure、连续凸近似和交替优化方法，将原问题转换为凸优化问题，并提出一种基于迭代的鲁棒资源分配算法。仿真结果表明，与非鲁棒算法对比，所提算法具有较好的吞吐量和鲁棒性，且中断概率减小2.39%。
- 认知无线电网络 /
- 反向散射通信 /
- 吞吐量最大化 /
- 鲁棒性
Abstract: To improve spectrum transmission efficiency and suppress the effect of channel uncertainties, a throughput-maximization algorithm is proposed for Cognitive Backscatter Communication with imperfect channel state information. Firstly, considering the constraints of the maximum transmit power of the Primary Base Station (PBS), transmission time, user quality of service, and bounded channel uncertainty, a multivariable coupled nonlinear robust throughput-maximization model is formulated by jointly optimizing the PBS’s beamforming vector, the reflection coefficient and the transmission time. Then, the original problem is transformed into a convex optimization problem by using the worst-case approach, the S-Procedure, successive convex approximation, alternating optimization, and an iteration-based robust resource allocation algorithm is proposed to solve it. Simulation results show that the proposed algorithm has better throughput and robustness compared with the non-robust algorithm, and the outage probability is reduced by 2.39%.
- Cognitive radio network /
- Backscatter communication /
- Throughput maximization /
- Robustness

HTML全文

图 1 认知反向散射通信网络

下载: 全尺寸图片幻灯片

图 2 系统吞吐量收敛图

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图 3 系统吞吐量与$R_k^{\min }$在不同${\varphi _k}$下的关系

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图 4 系统吞吐量与$I_m^{\max }$在不同$ {\epsilon}_{k} $下的关系

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图 5 系统吞吐量与不确定性参数和$ {P^{\max }} $的关系

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图 6 中断概率与$ {\epsilon}_{k} $在不同算法下的关系

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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：$ N $, $ M $, $ K $, $T$, $ {\sigma ^2} $, $ {{\boldsymbol{f}}_k} $, $ {{\boldsymbol{f}}_{\text{p}}} $, $ {h_k} $, $ {h_{k,m}} $, $ {P^{\max }} $, $ R_k^{\min } $, $ I_m^{\max } $, $R_{{\text{sum}}}^{\left( 0 \right)}$; ${L_{\max }}$是最大迭代次数，$\eta > 0$是收敛精度，$l = 0$是初始迭代值；
(1) While$\left\| {R_{ {\text{sum} } }^{\left( l \right)} - R_{ {\text{sum} } }^{\left( {l - 1} \right)} } \right\| \ge \eta$或$l \le {L_{\max } }$do
(2) 设置迭代次数$l = l + 1$；
(3) 固定$ \tau _k^{\left( {l - 1} \right)} $和$ \beta _k^{\left( {l - 1} \right)} $，根据式(20)计算$ {\boldsymbol{W}}_m^{\left( l \right)} $。若$ {\boldsymbol{W}}_m^{\left( l \right)} $的秩为1，应用特征值分解法可得到可行解，其中$ {\boldsymbol{W}}_m^{\left( l \right)} = {\boldsymbol{w}}_m^{\left( l \right)}{\boldsymbol{w}}_m^{\left( l \right){\text{H}}} $，若$ {\boldsymbol{W}}_m^{\left( l \right)} $的秩大于1，采用高斯随机化方法求近似解。
(4) 固定$ {\boldsymbol{W}}_m^{\left( l \right)} $，根据式(24)计算$ \tau _k^{\left( l \right)} $，$ \beta _k^{\left( l \right)} $；
(5) 更新吞吐量$R_{{\text{sum}}}^{\left( l \right)}$
(6) End While

下载: 导出CSV

参考文献(25)

[1]	HAYKIN S. Cognitive radio: Brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201–220. doi: 10.1109/JSAC.2004.839380
[2]	XU Yongjun, ZHAO Xiaohui, and LIANG Yingchang. Robust power control and beamforming in cognitive radio networks: A survey[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 1834–1857. doi: 10.1109/COMST.2015.2425040
[3]	LI Dong. Hybrid active and passive antenna selection for backscatter-assisted MISO systems[J]. IEEE Transactions on Communications, 2020, 68(11): 7258–7269. doi: 10.1109/TCOMM.2020.3014917
[4]	徐勇军, 杨浩克, 叶迎晖, 等. 反向散射通信网络资源分配综述[J]. 物联网学报, 2021, 5(3): 56–69. doi: 10.11959/j.issn.2096-3750.2021.00215 XU Yongjun, YANG Haoke, YE Yinghui, et al. A survey on resource allocation in backscatter communication networks[J]. Chinese Journal on Internet of Things, 2021, 5(3): 56–69. doi: 10.11959/j.issn.2096-3750.2021.00215
[5]	LI Dong. Backscatter communication powered by selective relaying[J]. IEEE Transactions on Vehicular Technology, 2020, 69(11): 14037–14042. doi: 10.1109/TVT.2020.3029340
[6]	LI Dong. Two birds with one stone: Exploiting decode-and-forward relaying for opportunistic ambient backscattering[J]. IEEE Transactions on Communications, 2020, 68(3): 1405–1416. doi: 10.1109/TCOMM.2019.2957490
[7]	XU Yongjun, GU Bowen, HU R Q, et al. Joint computation offloading and radio resource allocation in MEC-based wireless-powered backscatter communication networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(6): 6200–6205. doi: 10.1109/TVT.2021.3077094
[8]	XU Yongjun, QIN Zhijin, GUI Guan, et al. Energy efficiency maximization in NOMA enabled backscatter communications with QoS guarantee[J]. IEEE Wireless Communications Letters, 2021, 10(2): 353–357. doi: 10.1109/LWC.2020.3031042
[9]	张倩倩. 认知反向散射通信互惠传输理论与资源配置方法研究[D]. [博士论文], 电子科技大学, 2021. ZHANG Qianqian. Research on mutualistic transmission theory and resource allocation method in cognitive backscatter communications[D]. [Ph. D. dissertation], University of Electronic Science and Technology of China, 2021.
[10]	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
[11]	LU Xiao, WANG Ping, LI Guangxia, et al. Short-packet backscatter assisted wireless-powered relaying with NOMA: Mode selection with performance estimation[J]. IEEE Transactions on Cognitive Communications and Networking, 2022, 8(1): 216–231. doi: 10.1109/TCCN.2021.3108158
[12]	XIAO Sa, GUO Huayan, and LIANG Yingchang. Resource allocation for full-duplex-enabled cognitive backscatter networks[J]. IEEE Transactions on Wireless Communications, 2019, 18(6): 3222–3235. doi: 10.1109/TWC.2019.2912203
[13]	KANG Xin, LIANG Yingchang, and YANG Jing. Riding on the primary: A new spectrum sharing paradigm for wireless-powered IoT devices[J]. IEEE Transactions on Wireless Communications, 2018, 17(9): 6335–6347. doi: 10.1109/TWC.2018.2859389
[14]	ZHUANG Yuandong, LI Xi, JI Hong, et al. Optimal resource allocation for RF-powered underlay cognitive radio networks with ambient backscatter communication[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15216–15228. doi: 10.1109/TVT.2020.3037152
[15]	LIU Xiaolan, GAO Yue, and HU Fengye. Optimal time scheduling scheme for wireless powered ambient backscatter communications in IoT networks[J]. IEEE Internet of Things Journal, 2019, 6(2): 2264–2272. doi: 10.1109/JIOT.2018.2889700
[16]	WANG Jie, YE Hanting, KANG Xin, et al. Cognitive backscatter NOMA networks with multi-slot energy causality[J]. IEEE Communications Letters, 2020, 24(12): 2854–2858. doi: 10.1109/LCOMM.2020.3019203
[17]	徐勇军, 杨浩克, 李国军, 等. 多标签无线供电反向散射通信网络能效优化算法[J]. 电子与信息学报, 2022, 44(10): 3492–3498. doi: 10.11999/JEIT210772 XU Yongjun, YANG Haoke, LI Guojun, et al. Energy-efficient optimization algorithm in multi-tag wireless-powered backscatter communication networks[J]. Journal of Electronics &Information Technology, 2022, 44(10): 3492–3498. doi: 10.11999/JEIT210772
[18]	YANG Gang, ZHANG Jiapeng, and LIANG Yingchang. Optimal beamforming in cooperative cognitive backscatter networks for wireless-powered IoT[C]. Proceedings of 2018 IEEE International Conference on Communication Systems (ICCS), Chengdu, China, 2018: 56–61.
[19]	徐勇军. 下垫式认知无线电网络动态资源分配问题研究[D]. [博士论文], 吉林大学, 2015. XU Yongjun. Research on dynamic resource allocation for underlay cognitive radio networks[D]. [Ph. D. dissertation], Jilin University, 2015.
[20]	XU Yongjun, XIE Hao, LIANG Chengchao, et al. Robust secure energy-efficiency optimization in SWIPT-aided heterogeneous networks with a nonlinear energy-harvesting model[J]. IEEE Internet of Things Journal, 2021, 8(19): 14908–14919. doi: 10.1109/JIOT.2021.3072965
[21]	徐勇军, 谷博文, 杨洋, 等. 基于不完美CSI的D2D通信网络鲁棒能效资源分配算法[J]. 电子与信息学报, 2021, 43(8): 2189–2198. doi: 10.11999/JEIT200587 XU Yongjun, GU Bowen, YANG Yang, et al. Robust energy-efficient resource allocation algorithm in D2D communication networks with imperfect CSI[J]. Journal of Electronics &Information Technology, 2021, 43(8): 2189–2198. doi: 10.11999/JEIT200587
[22]	NG D W K, LO E S, and SCHOBER R. Robust beamforming for secure communication in systems with wireless information and power transfer[J]. IEEE Transactions on Wireless Communications, 2014, 13(8): 4599–4615. doi: 10.1109/TWC.2014.2314654
[23]	BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004.
[24]	ZHENG Yuan, BI Suzhi, ZHANG Y J A, et al. Joint beamforming and power control for throughput maximization in IRS-assisted MISO WPCNs[J]. IEEE Internet of Things Journal, 2021, 8(10): 8399–8410. doi: 10.1109/JIOT.2020.3045703
[25]	CUI Miao, ZHANG Guangchi, and ZHANG Rui. Secure wireless communication via intelligent reflecting surface[J]. IEEE Wireless Communications Letters, 2019, 8(5): 1410–1414. doi: 10.1109/LWC.2019.2919685