To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Manuscript show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies. Xianfu Chen (M'13) received the Ph.D. degree in signal and information processing from the where he is currently a Senior Scientist. His current research interests include wireless communications and networking, with emphasis on human-level and artificial intelligence for resource awareness in next-generation communication networks. Honggang Zhang (M'01-SM'11) is a Full Professor with the
In REF , authors propose a user-centric computation offloading scheme for virtual edge computing systems based on double Q-learning.
Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning
IEEE Internet of Things Journal
IEEE Internet of Things Journal
Computer Science Mathematics