Throughput Maximization Algorithm for Cognitive Backscatter Communication with Imperfect CSI

XU Yongjun; JIANG Siqiao; WANG Gongpu; YANG Gang; LI Dong; HUANG Dong

doi:10.11999/JEIT221483

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2325-2333

XU Yongjun, JIANG Siqiao, WANG Gongpu, YANG Gang, LI Dong, HUANG Dong. Throughput Maximization Algorithm for Cognitive Backscatter Communication with Imperfect CSI[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2325-2333. doi: 10.11999/JEIT221483

Citation:

XU Yongjun, JIANG Siqiao, WANG Gongpu, YANG Gang, LI Dong, HUANG Dong. Throughput Maximization Algorithm for Cognitive Backscatter Communication with Imperfect CSI[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2325-2333. doi: 10.11999/JEIT221483

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Throughput Maximization Algorithm for Cognitive Backscatter Communication with Imperfect CSI

doi: 10.11999/JEIT221483

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)

Received Date: 2022-11-28
Rev Recd Date: 2023-04-12

Available Online: 2023-04-17

Publish Date: 2023-07-10

Abstract

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

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References(25)

References

[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