| 基于不完美CSI的认知反向散射通信吞吐量最大化算法 |
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| 引用本文： | 徐勇军, 姜思巧, 王公仆, 杨刚, 李东, 黄东. 基于不完美CSI的认知反向散射通信吞吐量最大化算法[J]. 电子与信息学报, 2023, 45(7): 2325-2333. doi: 10.11999/JEIT221483 |
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| 作者姓名： | 徐勇军 姜思巧 王公仆 杨刚 李东 黄东 |
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| 作者单位： | 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 |
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| 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) |
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| 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%。
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| 关 键 词： | 认知无线电网络 反向散射通信 吞吐量最大化 鲁棒性 |
| 收稿时间： | 2022-11-28 |
| 修稿时间： | 2023-04-12 |
Throughput Maximization Algorithm for Cognitive Backscatter Communication with Imperfect CSI |
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| 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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| Authors: | XU Yongjun JIANG Siqiao WANG Gongpu YANG Gang LI Dong HUANG Dong |
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| Affiliation: | 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 |
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| 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%. |
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| Keywords: | Cognitive radio network Backscatter communication Throughput maximization Robustness |
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