This paper considers an energy harvesting (EH) based multiuser mobile edge computing (MEC) system, where each user utilizes the harvested energy from renewable energy sources to execute its computation tasks via computation offloading and local computing. Towards maximizing the system's weighted computation rate (i.e., the number of weighted users' computing bits within a finite time horizon) subject to the users' energy causality constraints due to dynamic energy arrivals, the decision for joint computation offloading and local computing over time is optimized {\em over time}. Assuming that the profile of channel state information and dynamic task arrivals at the users is known in advance, the weighted computation rate maximization problem becomes a convex optimization problem. Building on the Lagrange duality method, the well-structured optimal solution is analytically obtained. Both the users' local computing and offloading rates are shown to have a monotonically increasing structure. Numerical results show that the proposed design scheme can achieve a significant performance gain over the alternative benchmark schemes.
翻译:本文件审议了基于能源的多用户移动边缘计算(EH)系统,每个用户都利用从可再生能源中获取的能源,通过计算卸载和本地计算来完成计算任务。为了尽量扩大该系统的加权计算率(即在有限时间范围内加权用户计算比特的数量),但须视用户因能源的动态到达而导致的能源因果关系限制而定,联合计算卸载和本地计算的决定随着时间的推移得到优化。假设事先知道频道国家信息和用户的动态任务到达情况,加权计算率最大化问题就成为一个螺旋体优化问题。在拉格朗双轨法的基础上,分析得出了结构完善的最佳解决办法。用户的本地计算率和卸载率都显示出一个单质增长的结构。数字结果显示,拟议的设计方案可以在替代基准方案下取得显著的业绩收益。