An Efficient Computation Offloading Strategy in Wireless Powered Mobile-Edge Computing Networks

The emergence of mobile edge computing (MEC) has improved the data processing capabilities of devices with limited computing resources. However, some tasks that require higher latency and energy consumption are still facing huge challenges. In this paper, for the time-varying wireless channel conditions, we proposed an effective method to perform offloading calculations on the computing tasks of wireless devices, that is, to distribute the tasks to the local of offload to the edge server under the premise of satisfying time delay and energy consumption. Based on this, we adopt the parallel calculation model of Deep Reinforcement Learning Optimal Stopping Theory (DRLOST), which is composed of two parts: offloading decision generation and deep reinforcement learning. The model uses a parallel deep neural network (DNN) to generate offloading decisions, and stores the generated offloading decisions in the memory according to the optimal stopping theory model parameters to further train the model. The simulation results show that the proposed algorithm can minimize delay time, and can respond quickly to tasks even in a fast-fading environment.