Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation-intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending the data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this article, an MEC scenario in the 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the nonorthogonal multiple access protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power-latency tradeoff, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation. Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power-latency tradeoff of the joint optimization of communication and computing resources and the superior performance over other benchmark schemes.

中文翻译：

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基于 NOMA 的网络中移动边缘计算卸载的动态功耗__atency 权衡


移动边缘计算（MEC）被认为是一种新兴技术，允许用户将计算密集型任务发送到部署在宏基站的MEC服务器。这个过程克服了移动设备（MD）的限制，而不是将数据发送到远离MD的云服务器。此外，MEC还可以减少云计算的延迟并提高服务质量。在本文中，考虑了5G网络中的MEC场景，其中多个用户向小区中的MEC服务器请求计算服务。我们假设用户可以通过非正交多址协议访问无线电频谱，并在用户侧采用排队论。主要目标是在缓冲队列的稳定性条件下最小化用户计算的总功耗，以研究功耗-延迟权衡，系统建模导致条件随机优化问题。为了获得最优解，我们采用李雅普诺夫优化方法以及逐次凸逼近。进行了大量的仿真，以说明所提出的算法在通信和计算资源联合优化的功率延迟权衡方面的优势以及相对于其他基准方案的优越性能。