报告题目: A Distributed Learning Framework for Multi-Access Edge Computing in Beyond 5G Networks
报告地点：zoom在线会议 会议ID：939 3075 5234 密码： nMsa4a
With the proliferation of smart devices, more and more mobile applications, such as location-based virtual/augmented reality and online gaming, are emerging and gaining popularity. To improve the computation qualities of service and experience, multi-access edge computing (MEC) is envisioned as a promising paradigm by providing computing capabilities in close proximity to mobile users (MUs). In addition to local processing, a resource-constrained MU can also offload the computation to resource-rich MEC servers for remote execution. Beyond 5G networks are expected to enhance the 5G capabilities towards the support of seamless wireless connectivity. The trend of merging wireless communications and MEC motivates the investigation of computation offloading in beyond 5G networks. Nevertheless, the design of computation offloading policies remains challenging due to the environmental uncertainties and the limited resource sharing. In this talk, we adopt a multi-agent Markov decision process to formulate the computation offloading problems, for which a distributed learning framework is proposed. We examine the potentials of the proposed distributed learning framework through use case studies.
报告人简介： 陈先福 高级科学家 博导 芬兰VTT技术研究中心
Xianfu Chen received his Ph.D. degree with honours in Signal and Information Processing, from the Department of Information Science and Electronic Engineering (ISEE) at Zhejiang University, Hangzhou, China, in March 2012. Since April 2012, Dr. Chen has been with the VTT Technical Research Centre of Finland, Oulu, Finland, where he is currently a Senior Scientist. He was a visiting scholar at the Wireless Networking, Signal Processing and Security Lab, University of Houston, USA, from March to April 2016, and the Department of Computer and Network Engineering, University of Electro-Communications, Japan, from June to August 2017. His research interests cover various aspects of wireless communications and networking, with emphasis on human-level and artificial intelligence for resource awareness in next-generation communication