IRS辅助的边缘智能系统中基于数据重要性感知的资源分配

doi:10.13190/j.jbupt.2020-162

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (6): 51-58.doi: 10.13190/j.jbupt.2020-162

IRS辅助的边缘智能系统中基于数据重要性感知的资源分配

田辉¹, 倪万里¹, 王雯¹, 郑景桁¹, 贺硕²

1. 北京邮电大学网络与交换技术国家重点实验室, 北京 100876;
2. 郑州大学信息工程学院, 郑州 450001

收稿日期:2020-09-02 出版日期:2020-12-28 发布日期:2020-11-30
作者简介:田辉(1963-),女,教授,博士生导师,E-mail:tianhui@bupt.edu.cn.
基金资助:
国家重点研发计划项目（2019YFC1511400）

Data-Importance-Aware Resource Allocation in IRS-Aided Edge Intelligent System

TIAN Hui¹, NI Wan-li¹, WANG Wen¹, ZHENG Jing-heng¹, HE Shuo²

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Received:2020-09-02 Online:2020-12-28 Published:2020-11-30

摘要/Abstract

摘要： 针对智能反射面（IRS）辅助的边缘智能系统中模型参数汇聚的问题，提出一种基于数据重要性感知的资源分配算法.利用凸优化和分支定界等方法交替优化用户的发射功率、传输次数和智能反射面的相移矩阵.仿真结果表明，所提算法能够基于本地数据的重要性差异有效汇聚分布式智能体的模型参数，并最大化加权和速率.

关键词: 智能反射面, 模型汇聚, 重要性感知, 资源分配

Abstract: In order to solve the problem of model aggregation in intelligent reflecting surface (IRS) aided edge intelligent system, a data-importance-aware resource allocation algorithm is proposed by using convex optimization and branch-and-bound methods to alternately design the user's uplink power, transmission time, and the phase shifts of IRS. Simulation results show that the proposed algorithm can effectively aggregate the model parameters of the distributed agents based on the importance difference of local data, and can maximize the uplink weighted sum rate.

Key words: intelligent reflecting surface, model aggregation, importance aware, resource allocation

中图分类号:

TN929.5

田辉, 倪万里, 王雯, 郑景桁, 贺硕. IRS辅助的边缘智能系统中基于数据重要性感知的资源分配[J]. 北京邮电大学学报, 2020, 43(6): 51-58.

TIAN Hui, NI Wan-li, WANG Wen, ZHENG Jing-heng, HE Shuo. Data-Importance-Aware Resource Allocation in IRS-Aided Edge Intelligent System[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(6): 51-58.

参考文献

[1] 张平, 牛凯, 田辉, 等. 6G移动通信技术展望[J]. 通信学报, 2019, 40(1):141-148. Zhang Ping, Niu Kai, Tian Hui, et al. Technology prospect of 6G mobile communications[J]. Journal on Communications, 2019, 40(1):141-148.
[2] 田辉, 范绍帅, 吕昕晨, 等. 面向5G需求的移动边缘计算[J]. 北京邮电大学学报, 2017, 40(2):1-10. Tian Hui, Fan Shaoshuai, Lü Xinchen, et al. Mobile edge computing for 5G requirements[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2):1-10.
[3] Yang Qiang, Liu Yang, Chen Tianjian, et al. Federated machine learning:concept and applications[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2019, 10(2):1-19.
[4] 易芝玲, 王森, 韩双锋, 等. 从5G到6G的思考:需求、挑战与技术发展趋势[J]. 北京邮电大学学报, 2020, 43(2):1-9. Yi Zhiling, Wang Sen, Han Shuangfeng, et al. From 5G to 6G:requirements, challenges and technical trends[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(2):1-9.
[5] 中国移动研究院. 2030+愿景与需求报告(V1)[EB/OL]. 2019(2019-11-15)[2020-08-24]. http://cmri.chinamobile.com/wp-content/up-loads/2019/11/2030愿景与需求报告.pdf.
[6] 梁应敞, 谭俊杰, Dusit Niyato. 智能无线通信技术研究概况[J]. 通信学报, 2020, 41(7):1-17. Liang Yingchang, Tan Junjie, Dusit N. Overview on intelligent wireless communication technology[J]. Journal on Communications, 2020, 41(7):1-17.
[7] Guo Jing, Durrani S, Zhou Xiangyun, et al. Massive machine type communication with data aggregation and resource scheduling[J]. IEEE Transactions on Communications, 2017, 65(9):4012-4026.
[8] Wu Qingqing, Zhang Rui. Towards smart and reconfigurable environment:intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020, 58(1):106-112.
[9] Jiang Tao, Shi Yuanming. Over-the-air computation via intelligent reflecting surfaces[C]//2019 IEEE Global Communications Conference (GLOBECOM). Waikoloa:IEEE Press, 2019:1-6.
[10] Zuo Jiakuo, Liu Yuanwei, Qin Zhijin, et al. Resource allocation in intelligent reflecting surface assisted NOMA systems[J]. IEEE Transactions on Communications, 2020, 68(11):7170-7183.
[11] Zhang Shuowen, Zhang Rui. Capacity characterization for intelligent reflecting surface aided MIMO communication[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8):1823-1838.
[12] Yang Yifei, Zheng Beixiong, Zhang Shuowen, et al. Intelligent reflecting surface meets OFDM:protocol design and rate maximization[J]. IEEE Transactions on Communications, 2020, 68(7):4522-4535.
[13] Yang Gang, Xu Xinyue, Liang Yingchang. Intelligent reflecting surface assisted non-orthogonal multiple access[C]//2020 IEEE Wireless Communications and Networking Conference (WCNC). Seoul:IEEE Press, 2020:1-6.
[14] Yu D, Park S H, Simeone O, et al. Optimizing over-the-air computation in IRS-aided C-RAN systems[C]//2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Atlanta:IEEE Press, 2020:1-6.
[15] She Changyang, Yang Chenyang, Quek T. Uplink transmission design with massive machine type devices in tactile internet[C]//2016 IEEE Globecom Workshops (GC Wkshps). Washington DC:IEEE Press, 2016:1-6.
[16] Boulogeorgos A, Alexiou A. Performance analysis of reconfigurable intelligent surface-assisted wireless systems and comparison with relaying[J]. IEEE Access, 2020, 8:94463-94483.
[17] Shi Qingjiang, Razaviyayn M, Luo Zhiquan, et al. An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel[J]. IEEE Transactions on Signal Processing, 2011, 59(9):4331-4340.
[18] Li Zhengfeng, Hua Meng, Wang Qingxia, et al. Weighted sum-rate maximization for multi-IRS aided cooperative transmission[J]. IEEE Wireless Communications Letters, 2020, 9(10):1620-1624.
[19] Boyd S, Vandenberghe L. Convex optimization[M]. Cambridge:Cambridge University Press, 2004:437-551.
[20] Jung C, Kim H, Lee T. A branch and bound algorithm for cyclic scheduling of timed petri nets[J]. IEEE Transactions on Automation Science and Engineering, 2015, 12(1):309-323.
[21] Chen Mingzhe, Yang Z, Saad W, et al. Performance optimization of federated learning over wireless networks[C]//2019 IEEE Global Communications Conference(GLOBECOM). Atlanta:IEEE Press, 2019:1-6.
[22] Choi H J, Lee K J, Song C, et al. Weighted sum rate maximization for multi-user multi-relay MIMO systems with direct links[C]//2013 IEEE 77th Vehicular Technology Conference (VTC Spring). Dresden:IEEE Press, 2013:1-6.

IRS辅助的边缘智能系统中基于数据重要性感知的资源分配

Data-Importance-Aware Resource Allocation in IRS-Aided Edge Intelligent System

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李民政曹宇青颜娟娟陆健. 多智能反射面辅助毫米波级联信道估计方法[J]. 北京邮电大学学报, 2023, 46(4): 15-20.
[2]	彭艺吴桐杨青青. 下行 RIS-NOMA 系统交替优化波束形成方法[J]. 北京邮电大学学报, 2023, 46(4): 64-69.
[3]	孙艳华, 乔兰, 王朱伟, 李萌, 司鹏搏. 智能反射面辅助MIMO系统混合波束赋形算法[J]. 北京邮电大学学报, 2023, 46(3): 7-12.
[4]	公雨魏翼飞. 一种集成学习辅助DDPG的资源优化算法[J]. 北京邮电大学学报, 2023, 46(2): 29-36.
[5]	彭维平杨玉莹宋成阎俊豪. VEC中多边缘节点协作卸载与资源分配算法[J]. 北京邮电大学学报, 2023, 46(2): 78-83.
[6]	魏明亮耿绥燕赵雄文胡玮范静怡. 超密集网络中移动边缘计算的资源分配和任务卸载联合优化研究[J]. 北京邮电大学学报, 2023, 46(2): 50-56.
[7]	李子能胡智群肖海林曾张帆. 基于智能反射面和协作干扰的无人机安全通信算法[J]. 北京邮电大学学报, 2023, 46(1): 69-76.
[8]	郭兴康孙君. 基于交替方向乘子法与深度强化学习算法的资源分配[J]. 北京邮电大学学报, 2022, 45(6): 126-130.
[9]	李美玲刘畅杨晓霞薛凯轩路兆铭. 认知NOMA系统中基于IRS的V2V网络物理层安全性能研究[J]. 北京邮电大学学报, 2022, 45(6): 131-137.
[10]	韩书君吕素玉许晓东陶小峰韩伯骁. 智能反射面辅助的短包通信系统：时延与安全性能分析[J]. 北京邮电大学学报, 2022, 45(6): 70-77.
[11]	郁小松, 朱青橙, 顾佳明, 赵永利, 张杰. 云边协同光量子物联网架构及资源分配[J]. 北京邮电大学学报, 2022, 45(3): 50-56.
[12]	杜梅, 周军华, 李敦桥, 陈士钊, 魏翼飞. MEC计算卸载与资源分配联合智能优化方案[J]. 北京邮电大学学报, 2022, 45(2): 65-71.
[13]	张雨晴, 李云, 黄鸿锐, 庄宏成. 异构网络中任务卸载与资源分配联合优化算法[J]. 北京邮电大学学报, 2022, 45(2): 91-97.
[14]	贾雨宁, 魏翼飞, 周军华. 基于SDN与NFV的服务功能链编排算法[J]. 北京邮电大学学报, 2022, 45(2): 85-90.
[15]	高晓娜, 卢光跃, 叶迎晖, 昝金枚. 认知反向散射网络通信容量公平的资源优化[J]. 北京邮电大学学报, 2021, 44(6): 26-32.