To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies. 978-1-5386-6358-5/18/$31.00 ©2018 IEEE
Deep Q-learning was used to minimize the task execution cost by optimizing offloading decision according to channel state information, queue state information, and energy queue state of the energy harvesting system REF .
Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning
2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)
2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)
Computer Science