Robust Resource Allocation Algorithm in MU-MISO Backscatter Communication Systems with Eavesdroppers
-
摘要: 针对反向散射通信系统信道估计不准、信息容易被窃听等问题，该文提出一种基于用户窃听的多用户-多输入单输出(MU-MISO)反向散射通信系统鲁棒资源分配算法，以提高系统传输鲁棒性与信息安全性。首先，考虑基站最大功率、时间分配、信道不确定性、能量收集和保密率等约束，建立一个MU-MISO的反向散射通信系统鲁棒资源分配问题。其次，基于非线性能量收集模型和有界球形信道不确定性模型，利用变量松弛法和S过程将原NP-hard问题转化为确定性问题，随后利用连续凸近似、半正定松弛与块坐标下降法将其转化为凸优化问题求解。仿真结果表明，与传统非鲁棒算法对比，所提算法具有较高的系统容量和较低的中断概率。Abstract: Focusing on the problems of inaccurate channel estimation and easy eavesdropping of information in backscatter communication systems, a robust resource allocation algorithm for Multi-User Multi-Input Single-Output (MU-MISO) backscatter communication systems based on user eavesdropping is proposed to improve the transmission robustness and information security of the systems. Firstly, considering the constraints on the maximum power of the base station, time allocation, channel uncertainties, energy collection, and security rate, a robust resource allocation problem for MU-MISO backscatter communication systems is established. Secondly, based on the nonlinear energy harvesting model and the bounded spherical uncertainty model, the original NP-hard problem is transformed into a deterministic one by using the variable relaxation and S-Procedure methods, and then it is transformed into a convex optimization problem by using successive convex approximation, semi-positive definite relaxation and block coordinate descent methods. Simulation results show that the proposed algorithm has higher system capacity and lower outage probability compared with the traditional non-robust algorithm.
-
算法1 基于块坐标下降法的鲁棒资源分配迭代算法 1. 初始化系统参数：$ K,\,M,\,{\beta _k},\,E_k^{\text{C}},\,\sigma _k^2,\,\sigma _e^2,\,T,\,\bar x_k^2,\,\bar x_k^3,\,\bar y_k^1,\,\bar \gamma _k^{\text{E}},\,\alpha _k^{\left( 0 \right)},\,{R^{{\text{sum}}(0)}} $；阈值：$r_k^{\min },\,{P^{\max }}$；估计误差上界：$\varepsilon $；收敛精度：$ \varpi $；
初始化迭代次数：$l{\text{ = }}1$。2. Repeat 3. 固定$ \alpha _k^{\left( 0 \right)} $，求解子问题1，获得$ {\boldsymbol{W}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{\left( l \right) * }} $。 if $ {\boldsymbol{W}}_k^{\left( l \right) * } $满足$ \text{Rank}\left({{\boldsymbol{W}}}_{k}^{\left(l\right)\ast }\right)=1 $，可以通过特征值分解获得最优波束向量，即${\boldsymbol{W}}_k^{\left( l \right) * }{\text{ = }}{\boldsymbol{w}}_k^{\left( l \right) * }{\boldsymbol{w}}_k^{\left( l \right) * {\text{H}}}$。 else if ${\boldsymbol{W}}_k^{\left( l \right) * }$的秩大于1，可以通过高斯随机化获得最优向量。 4. 固定${\boldsymbol{W}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{\left( l \right) * }}$，求解子问题2，获得$ \alpha _k^{\left( l \right) * } $。 5. 计算$ {R^{{\text{sum}}(l)}} $，并且更新迭代次数$l = l + 1$。 6. Until $ \left| {{R^{{\text{sum}}(l)}} - {R^{{\text{sum}}(l - 1)}}} \right| \le \varpi $。 7. Return $ {\boldsymbol{w}}_k^{{\text{opt}}}{\text{ = }}{\boldsymbol{w}}_k^{\left( l \right) * },\,{{\boldsymbol{Z}}^{{\text{opt}}}} = {{\boldsymbol{Z}}^{\left( l \right) * }},\,\alpha _k^{{\text{opt}}} = \alpha _k^{\left( l \right) * },\,{R^{{\text{sum}}}} = {R^{{\text{sum}}(l)}} $。 
下载: 导出CSV
-
[1] 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.2096-3750.2021.00215XU 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 [2] VAN HUYNH N, HOANG D T, LU Xiao, et al. Ambient backscatter communications: A contemporary survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(4): 2889–2922. doi: 10.1109/COMST.2018.2841964 [3] LONG Ruizhe, LIANG Yingchang, GUO Huayan, et al. Symbiotic radio: A new communication paradigm for passive internet of things[J]. IEEE Internet of Things Journal, 2020, 7(2): 1350–1363. doi: 10.1109/JIOT.2019.2954678 [4] WANG Pu, YAN Zheng, WANG Ning, et al. Resource allocation optimization for secure multidevice wirelessly powered backscatter communication with artificial noise[J]. IEEE Transactions on Wireless Communications, 2022, 21(9): 7794–7809. doi: 10.1109/TWC.2022.3162137 [5] YANG Haohang, YE Yinghui, and CHU Xiaoli. Max-min energy-efficient resource allocation for wireless powered backscatter networks[J]. IEEE Wireless Communications Letters, 2020, 9(5): 688–692. doi: 10.1109/LWC.2020.2965942 [6] XIAO Sa, GUO Huayan, and LIANG Yinghchang. 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 [7] KE Feng, PENG Yiming, PENG Yingru, et al. Resource allocation for MIMO full-duplex backscatter assisted wireless-powered communication network with finite alphabet inputs[J]. IEEE Transactions on Communications, 2021, 69(2): 1275–1289. doi: 10.1109/TCOMM.2020.3035233 [8] YE Yinghui, SHI Liqin, HU R Q, et al. Energy-efficient resource allocation for wirelessly powered backscatter communications[J]. IEEE Communications Letters, 2019, 23(8): 1418–1422. doi: 10.1109/LCOMM.2019.2920834 [9] 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 [10] 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 [11] XU Yongjun and GUI Guan. Optimal resource allocation for wireless powered multi-carrier backscatter communication networks[J]. IEEE Wireless Communications Letters, 2020, 9(8): 1191–1195. doi: 10.1109/LWC.2020.2985010 [12] YANG Gang, YUAN Dongdong, LIANG Yingchang, et al. Optimal resource allocation in full-duplex ambient backscatter communication networks for wireless-powered IoT[J]. IEEE Internet of Things Journal, 2019, 6(2): 2612–2625. doi: 10.1109/JIOT.2018.2872515 [13] LI Dong. Backscatter communication via harvest-then-transmit relaying[J]. IEEE Transactions on Vehicular Technology, 2020, 69(6): 6843–6847. doi: 10.1109/TVT.2020.2991227 [14] WANG Pu, YAN Zheng, and ZENG Kai. BCAuth: Physical layer enhanced authentication and attack tracing for backscatter communications[J]. IEEE Transactions on Information Forensics and Security, 2022, 17: 2818–2834. doi: 10.1109/TIFS.2022.3195407 [15] LI Xinrui, WANG Wei, ZHANG Miao, et al. Robust secure beamforming for SWIPT-aided relay systems with full-duplex receiver and imperfect CSI[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2): 1867–1878. doi: 10.1109/TVT.2019.2961449 [16] 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 [17] ZHOU Shaoqing, XU Wei, WANG Kezhi, et al. Ergodic rate analysis of cooperative ambient backscatter communication[J]. IEEE Wireless Communications Letters, 2019, 8(6): 1679–1682. doi: 10.1109/LWC.2019.2936196 [18] 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 [19] 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 [20] WANG Shaohang and WANG Baoyun. Robust secure transmit design in MIMO channels with simultaneous wireless information and power transfer[J]. IEEE Signal Processing Letters, 2015, 22(11): 2147–2151. doi: 10.1109/LSP.2015.2464791 [21] WANG Feng, XU Jie, and DING Zhiguo. Multi-antenna NOMA for computation offloading in multiuser mobile edge computing systems[J]. IEEE Transactions on Communications, 2019, 67(3): 2450–2463. doi: 10.1109/TCOMM.2018.2881725 [22] BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004. [23] LUO Zhiquan, MA W K, SO A M C, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20–34. doi: 10.1109/MSP.2010.936019 [24] BIBBY J. Axiomatisations of the average and a further generalisation of monotonic sequences[J]. Glasgow Mathematical Journal, 1974, 15(1): 63–65. doi: 10.1017/S0017089500002135 [25] LU Yang, XIONG Ke, FAN Pingyi, et al. Coordinated beamforming with artificial noise for secure SWIPT under non-linear EH model: Centralized and distributed designs[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(7): 1544–1563. doi: 10.1109/JSAC.2018.2824759 -
图(7) / 表(1)
计量
- 文章访问数: 228
- HTML全文浏览量: 168
- PDF下载量: 55
- 被引次数: 0
下载:
