智能反射面赋能无人机边缘网络计算卸载方案

doi:10.11959/j.issn.1000-436x.2022196

通信学报 ›› 2022, Vol. 43 ›› Issue (10): 223-233.doi: 10.11959/j.issn.1000-436x.2022196

• 学术通信 • 上一篇

智能反射面赋能无人机边缘网络计算卸载方案

李斌¹, 刘文帅¹, 谢万城¹, 费泽松²

¹ 南京信息工程大学计算机与软件学院，江苏南京 210044
² 北京理工大学信息与电子学院，北京 100081

修回日期:2022-09-26 出版日期:2022-10-25 发布日期:2022-10-01
作者简介:李斌（1987− ），男，山东济宁人，博士，南京信息工程大学副教授、硕士生导师，主要研究方向为无人机通信、移动边缘计算等
刘文帅（1996− ），男，河北保定人，南京信息工程大学硕士生，主要研究方向为移动边缘计算、智能反射面等
谢万城（2001− ），男，湖北宜昌人，南京信息工程大学硕士生，主要研究方向为移动边缘计算、智能反射面等
费泽松（1977− ），男，安徽合肥人，博士，北京理工大学教授、博士生导师，主要研究方向为无线通信、多媒体信号处理等
基金资助:
国家重点研发计划基金资助项目(2020YFB1806900);国家自然科学基金资助项目(62101277);江苏省自然科学基金资助项目(BK20200822);江苏省研究生科研与实践创新计划基金资助项目(KYCX22_1204)

Computation offloading scheme for RIS-empowered UAV edge network

Bin LI¹, Wenshuai LIU¹, Wancheng XIE¹, Zesong FEI²

¹ School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
² School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Revised:2022-09-26 Online:2022-10-25 Published:2022-10-01
Supported by:
The National Key Research and Development Program of China(2020YFB1806900);The National Natural Science Foundation of China(62101277);The Natural Science Foundation of Jiangsu Province(BK20200822);Postgraduate Research ＆ Practice Innovation Program of Jiangsu Province(KYCX22_1204)

摘要/Abstract

摘要：

摘要：针对无人机辅助边缘计算城市场景，因无人机与地面用户之间的任务卸载链路易受障碍物遮挡而造成卸载速率低的问题，提出了一种智能反射面赋能的无人机边缘计算部分任务卸载方案。联合考虑任务分配、用户发射功率、智能反射面相移矩阵、无人机计算资源分配以及无人机轨迹，建立了一个用户最小平均数据吞吐量最大化问题。由于该问题是一个随机优化问题，且优化变量之间密切耦合，难以直接求解。因此，首先通过利用数学期望的性质，将随机优化问题转换为确定性优化问题；其次，利用块坐标下降算法将确定性优化问题分解为3个子问题，并通过引入辅助变量、利用连续凸近似和半定松弛技术将非凸问题转换为凸优化问题，进而得到问题的近似次优解。仿真结果表明，所提方案具有良好的收敛性能，并有效地提高了地面用户的平均数据吞吐量。

关键词: 无人机通信, 移动边缘计算, 智能反射面, 计算卸载, 资源分配

Abstract:

In order to address the challenge of low offloading rate caused by the obstacles blocking in the links between unmanned aerial vehicle (UAV) and ground users (GU) in urban scene, a partial task offloading scheme for UAV-enabled mobile edge computing with the aid of reconfigurable intelligence surface was proposed.A nonconvex and multivariable coupling stochastic optimization problem was formulated by the joint design of the computation task allocation, the transmit power of GU, the phase shift of RIS, UAV computation resource, and UAV trajectory, aiming at maximizing the minimum average data throughput of GU.By leveraging the properties of mathematical expectation, the stochastic optimization problem was transformed into a deterministic optimization problem.Then, the deterministic optimization problem was decomposed into three subproblems by using the block coordinate descent (BCD) algorithm.By introducing auxiliary variables, the nonconvex problems were transformed into convex optimization problems via the successive convex approximation and semidefinite relaxation, and then the approximate suboptimal solution of the original problem was obtained.The simulation results show that the proposed algorithm has good convergence performance and effectively improves the average data throughput of GU.

Key words: UAV communication, mobile edge computing, reconfigurable intelligent surface, computation offloading, resource allocation

中图分类号:

TN92

李斌, 刘文帅, 谢万城, 费泽松. 智能反射面赋能无人机边缘网络计算卸载方案[J]. 通信学报, 2022, 43(10): 223-233.

Bin LI, Wenshuai LIU, Wancheng XIE, Zesong FEI. Computation offloading scheme for RIS-empowered UAV edge network[J]. Journal on Communications, 2022, 43(10): 223-233.

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参考文献 23

[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 P , NIU K , TIAN H ,et al. Technology prospect of 6G mobile communications[J]. Journal on Communications, 2019,40(1): 141-148.
[2]	LETAIEF K B , SHI Y M , LU J M ,et al. Edge artificial intelligence for 6G:vision,enabling technologies,and applications[J]. IEEE Journal on Selected Areas in Communications, 2022,40(1): 5-36.
[3]	陈新颖, 盛敏, 李博 ,等. 面向6G的无人机通信综述[J]. 电子与信息学报, 2022,44(3): 781-789.
	CHEN X Y , SHENG M , LI B ,et al. Survey on unmanned aerial vehicle communications for 6G[J]. Journal of Electronics ＆ Information Technology, 2022,44(3): 781-789.
[4]	WANG J , NA Z Y , LIU X . Collaborative design of multi-UAV trajectory and resource scheduling for 6G-enabled Internet of things[J]. IEEE Internet of Things Journal, 2021,8(20): 15096-15106.
[5]	YOU X H , WANG C X , HUANG J ,et al. Towards 6G wireless communication networks:vision,enabling technologies,and new paradigm shifts[J]. Science China Information Sciences, 2020,64(1): 1-74.
[6]	LI M S , CHENG N , GAO J ,et al. Energy-efficient UAV-assisted mobile edge computing:resource allocation and trajectory optimization[J]. IEEE Transactions on Vehicular Technology, 2020,69(3): 3424-3438.
[7]	ZHAO M X , LI W T , BAO L Y ,et al. Fairness-aware task scheduling and resource allocation in UAV-enabled mobile edge computing networks[J]. IEEE Transactions on Green Communications and Networking, 2021,5(4): 2174-2187.
[8]	XU Y , ZHANG T K , YANG D C ,et al. Joint resource and trajectory optimization for security in UAV-assisted MEC systems[J]. IEEE Transactions on Communications, 2021,69(1): 573-588.
[9]	WANG L , WANG K Z , PAN C H ,et al. Multi-agent deep reinforcement learning-based trajectory planning for multi-UAV assisted mobile edge computing[J]. IEEE Transactions on Cognitive Communications and Networking, 2021,7(1): 73-84.
[10]	崔高峰, 徐媛媛, 张尚宏 ,等. 基于最小能耗的多无人机无线网络安全数据卸载策略[J]. 通信学报, 2021,42(5): 51-62.
	CUI G F , XU Y Y , ZHANG S H ,et al. Secure data offloading strategy for multi-UAV wireless networks based on minimum energy consumption[J]. Journal on Communications, 2021,42(5): 51-62.
[11]	屈毓锛, 秦蓁, 马靖豪 ,等. 面向空地协同移动边缘计算的服务布置策略[J]. 计算机学报, 2022,45(4): 781-797.
	QU Y B , QIN Z , MA J H ,et al. Service provisioning for air-ground collaborative mobile edge computing[J]. Chinese Journal of Computers, 2022,45(4): 781-797.
[12]	YANG Z Y , BI S Z , ZHANG Y J A . Online trajectory and resource optimization for stochastic UAV-enabled MEC systems[J]. IEEE Transactions on Wireless Communications, 2022,21(7): 5629-5643.
[13]	CAO X L , YANG B , HUANG C W ,et al. Converged reconfigurable intelligent surface and mobile edge computing for space information networks[J]. IEEE Network, 2021,35(4): 42-48.
[14]	HUANG C W , ZAPPONE A , ALEXANDROPOULOS G C ,et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication[J]. IEEE Transactions on Wireless Communications, 2019,18(8): 4157-4170.
[15]	JI B F , WANG Y N , SONG K ,et al. A survey of computational intelligence for 6G:key technologies,applications and trends[J]. IEEE Transactions on Industrial Informatics, 2021,17(10): 7145-7154.
[16]	BAI T , PAN C H , DENG Y S ,et al. Latency minimization for intelligent reflecting surface aided mobile edge computing[J]. IEEE Journal on Selected Areas in Communications, 2020,38(11): 2666-2682.
[17]	DAI Y Y , GUAN Y L , LEUNG K K ,et al. Reconfigurable intelligent surface for low-latency edge computing in 6G[J]. IEEE Wireless Communications, 2021,28(6): 72-79.
[18]	WU Q Q , ZHANG R . Towards smart and reconfigurable environment:intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020,58(1): 106-112.
[19]	LIU X , LIU Y W , CHEN Y . Machine learning empowered trajectory and passive beamforming design in UAV-RIS wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2021,39(7): 2042-2055.
[20]	MU X D , LIU Y W , GUO L ,et al. Intelligent reflecting surface enhanced multi-UAV NOMA networks[J]. IEEE Journal on Selected Areas in Communications, 2021,39(10): 3051-3066.
[21]	REN H , ZHANG Z K , PENG Z J ,et al. Energy minimization in RIS-assisted UAV-enabled wireless power transfer systems[J]. IEEE Internet of Things Journal, 2022:doi.org/10.1109/JIOT.2022.3150178.
[22]	MEI H B , YANG K , LIU Q ,et al. 3D-trajectory and phase-shift design for RIS-assisted UAV systems using deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2022,71(3): 3020-3029.
[23]	BOYD S , VANDENBERGHE L . Convex optimization[M]. Cambridge: Cambridge University Press, 2004.

智能反射面赋能无人机边缘网络计算卸载方案

Computation offloading scheme for RIS-empowered UAV edge network

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