双智能反射面辅助的绿色物联网边缘计算吞吐量研究

Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity

为解决计算密集型应用的终端物联网用户设备微电池能量难题,研究绿色可再生能量收集技术和双重构智能反射面技术赋能边缘计算,构建双智能反射面辅助的绿色物联网边缘计算系统,有效延长终端物联网用户设备计算寿命,提高系统计算吞吐量。首先,建立双智能反射面辅助的多用户级联衰落信道模型,建立绿色可再生能量收集的多时隙随机到达模型,建模物联网终端设备的能量供需因果约束条件。其次,以系统计算吞吐量最大化为准则,建模终端设备本地计算速率、边缘计算卸载功率、智能反射面相位的联合优化设计问题;该设计问题隶属一类复杂的非凸优化问题。为此,采用轻量级的多阶段优化技术,快速迭代设计本地计算、计算卸载、智能反射面相移等变量,完成绿色物联网边缘计算系统设计。实验结果表明,在较少的系统计算时间下,本文所提方案与基于半定松弛算法的性能增益相当,且优于已有的基准方案。

In order to solve the micro battery energy problem of terminal Internet of Things user devices in computation-intensive applications,green renewable energy harvesting technology and double reconfigurable intelligent surfaces(RIS) technology enabling edge computing were studied and a green Internet of Things edge computing system assisted by double-RIS was constructed,effectively extending the computing life of terminal Internet of Things user devices and improving system computation capacity.Firstly,a multi-user cascade fading channel model assisted by double-RIS was established,and a multi-slot random arrival model of green renewable energy harvesting was established to model the causal constraints of energy supply and demand of Internet of Things terminal devices.Secondly,based on the maximization of system computation capacity,the joint optimization design problem of terminal local computing rate,edge computing offloading power and phase shift of RISs was modeled.This design problem belongs to a class of complex non-convex optimization problem.To this end,lightweight multi-stage optimization technology was adopted to rapidly and iteratively design variables such as local computation,computation offloading and phase shift of RISs,etc,to complete the design of green Internet of Things edge computing system.The experimental results show that the performance gains of the proposed scheme are better than the existing benchmark schemes,and the proposed scheme is equivalent to the scheme based on semidefinite relaxation algorithm under less system computing time.