… Mobile edge computing (MEC) emerges recently as a promising solution to relieve
resource-limited mobile … In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) …

… The rapid growth of mobile internet services has yielded a … Mobile Edge Computing (MEC),
which enables mobile terminals to offload computation tasks to servers located at the edge of …

… computing resources allocated to each user are constantly changing. Therefore, this paper
focuses on multi-user … with the user is migrated to an optimal edge node closer to the user to …

… (MEC) is proposed to provide universal and flexible computing services at the edge of
the … multiuser computation offloading in the MEC. For this problem, we formulate a multi-user …

… , we develop a deep reinforcement learning algorithm, … use computer simulation to compare
the effectiveness of the proposed MARL algorithms with conventional Q-learning and deep Q…

… allocation in multi-user multi-channel mobile edge computing (… mobile edge computing: A
deep reinforcement learning approach,” IEEE Transactions on Emerging Topics in Computing…

… In this paper, a deep reinforcement learning based algorithm is used to schedule and
allocate delay-sensitive tasks on the server side of a multi-user MEC system. The goal is to …

… Wang, “Decentralized computation offloading for multiuser mobile edge computing: A
deep reinforcement learning approach,” EURASIP J. Wireless Commun. Netw., vol. …

… In this work, we studied multi-server multi-user multi-task computation offloading for MEC
networks, with … By taking advantage of deep reinforcement learning, we further investigated the …

… and multi-user scheduling algorithm for IoT edge computing … this MDP problem for the
multi-user resource control is the … this challenge, we use the deep reinforcement learning (RL) …