智能超表面辅助边缘计算任务卸载增强技术

Enhanced Task Offloading for RIS-Assisted Mobile Edge Computing Networks

针对无线信道的随机性和不确定性导致的MEC网络任务卸载时传输速率低的问题,提出一种RIS辅助MEC方案,利用RIS增强链路的能力来支持任务卸载。通过对用户发射功率、任务卸载量、MEC计算资源调度、RIS相移矩阵等进行联合优化,最小化服务总时延。由于原问题是具有高度耦合变量的非凸问题,借助块坐标下降法将原问题分解为三个子问题,并利用拉格朗日对偶法、逐次凸近似法和交替方向乘子法进行求解。最后设计了一种交替算法,迭代求解近似最优解。仿真结果表明,与其他基准方案相比,该方案在降低服务时延方面具有更好的性能。

In addressing the challenges posed by the randomness and uncertainty of wireless channels,which result in reduced transmission rates during task ofloading in mobile edge computing(MEC)networks,a reconfigurable intelligent surface(RIS)-assisted MEC scheme is introduced.This scheme capitalizes on RIS to bolster link capabilities,facilitating effective task offloading.By jointly optimizing user transmit power,task offloading bits,computing resource scheduling,and the RIS phase shift matrix,we aim to minimize the overall service latency.Given the inherent non-convexity of the primary problem characterized by its highly coupled variables,it is decomposed into three sub-problems using the block coordinate descent method.Solutions are subsequently derived employing the Lagrangian dual method,successive convex approximation,and the alternating direction method of multipliers.An iterative algorithm is designed to approximate the optimal solution.Simulation results demonstrate the superiority of the proposed scheme in reducing overall service latency compared to benchmarks.