摘要
针对无人机(UAV)面对复杂时变的衰落信道对无线传输造成不利影响这一问题,构建了可重构智能表面(RIS)辅助UAV边缘计算网络中UAV轨迹、RIS相移、卸载时隙分配、CPU频率分配和用户设备(UE)传输功率的联合优化问题。为了求解构建的问题,对UE和UAV的任务队列稳定性约束进行转化,将多时隙随机性优化问题转化为每个时隙的确定性优化问题,提出了基于李雅普诺夫优化和块坐标下降(BCD)法的任务卸载与资源分配(JORL)方法。首先基于三角不等式求解RIS相移并求得闭合表达式;然后使用非凸转凸问题的技术求解卸载时隙分配、CPU频率分配和UE传输功率;最后基于连续凸近似方法求解UAV轨迹。仿真结果表明,JORL在保证任务队列稳定性和降低能耗方面有较优越的性能。
To address the problem that unmanned aerial vehicle(UAV)face complex time-varying fading channels,which could affect wireless transmission,a joint optimization problem of UAV's trajectory,reconfigurable intelligent surface(RIS)phase shift,offloading slot allocation,CPU frequency allocation,and user equipment transmission power was constructed.In order to solve the constructed problem,the stability constraints of UE and UAV task queues were transformed,and the multi-timeslot stochastic optimization problem was transformed into a deterministic optimization problem for each time slot.A JORL optimization method based on Lyapunov optimization and block coordinate descent(BCD)method was proposed.Firstly,the phase shift of RIS was solved based on the triangle inequality and the closure expression was obtained.Then the technology of transforming the non-convex into the convex problem was used to solve the offloading slot allocation,CPU frequency allocation and user equipment transmission power.Finally,the trajectory of UAV was solved based on successive convex approximation(SCA)method.Simulation results show that JORL has better performance in ensuring queue stability and reducing energy consumption.
作者
邝祝芳
郭宇敬
邓晓衡
KUANG Zhufang;GUO Yujing;DENG Xiaoheng(School of Computer and Mathematics,Central South University of Forestry and Technology,Changsha 410004,China;School of Electronic Information,Central South University,Changsha 410083,China)
出处
《通信学报》
EI
CSCD
北大核心
2024年第9期258-273,共16页
Journal on Communications
基金
国家自然科学基金资助项目(No.62072477)
国家重点研发计划基金资助项目(No.2023YFD2201703)
湖南省自然科学基金资助项目(No.2024JJ5648)
湖南省教育厅优秀青年基金资助项目(No.22C0132)。
关键词
无人机
边缘计算
可重构智能表面
任务队列
李雅普诺夫优化
unmanned aerial vehicle
edge computing
reconfigurable intelligent surface
task queue
Lyapunov optimization
