Xiaofei Wang

TL;DR: A novel edge caching scheme based on the concept of content-centric networking or information-centric networks is proposed and evaluated, using trace-driven simulations to evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks.

TL;DR: In this paper, the authors proposed to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with mobile edge systems for optimizing mobile edge computing, caching and communication, and designed the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load.

TL;DR: By consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

TL;DR: In this paper, a survey on the relationship between edge intelligence and intelligent edge computing is presented, and the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework, challenges and future trends of more pervasive and fine-grained intelligence.

TL;DR: This paper surveys and discusses various remarkable techniques toward green mobile networks to date, mainly targeting mobile cellular networks, and summarizes the current research projects related to greenMobile networks, along with the taxonomy of energy-efficiency metrics.