Lixing Chen

TL;DR: In this paper, the authors investigated the problem of dynamic service caching in MEC-enabled dense cellular networks and proposed an efficient online algorithm, called OREO, which jointly optimizes service caching and task offloading to address service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination.

TL;DR: In this article, an efficient reinforcement learning-based resource management algorithm was proposed to minimize the long-term system cost, including both service delay and operational cost, by using a decomposition of the (offline) value iteration and (online) reinforcement learning.

TL;DR: In this paper, the authors investigated the problem of dynamic service caching in MEC-enabled dense cellular networks and proposed an efficient online algorithm, called OREO, which jointly optimizes service caching and task offloading to address service heterogeneity, unknown system dynamics, spatial demand coupling and decentralized coordination.

TL;DR: In this article, a peer offloading game among small-cell base stations (SBSs) is proposed to maximize the long-term system performance while keeping the energy consumption of SBSs below individual longterm constraints.

TL;DR: This paper proposes an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost).