Tiankui Zhang is a professor and doctoral advisor in School of Information and Communication Engineering at Beijing University of Posts and Telecommunications (BUPT), Senior member of the Institute of Electrical and Electronics Engineers (IEEE), Member of the Board of Directors of the 5G Application Industry Alliance, Member of the Artificial Intelligence Society and Member of the Technical Committee of the BUPT-Telecom Visual Intelligence Joint Laboratory. His current main research areas are future network convergence and management and mobile network key technologies including emergency communication network management, UAV communications and networks, mobile edge computing and caching, content-centric mobile network architecture and caching, large-scale antennas and collaborative communication, wireless resource management technology, etc. He has presided over and participated in 10 national projects such as the National Natural Science Foundation of China and led 2 projects of the Beijing Natural Science Foundation and 1 major science and technology R&D project in Jiangxi Province. He has cooperated with leading institutes such as the China Academy of Information and Communication Technology and China Unicom Network Technology Research Institute to complete 28 3GPP standard proposals, 2 ETSI standards and 5 domestic industry standards. He has published more than 120 academic papers in domestic and international journals and academic conferences, published 1 monograph and applied for 72 patents (45 patents granted). He has won the second prize of Science and Technology Progress of Jiangxi Province in 2020 and won two best paper awards of international conference.
Speech Title: Joint Computing Offloading and Trajectory for Multi-UAV Enabled MEC Systems
The cooperation of multiple unmanned aerial vehicles (UAVs) is investigated to provide auxiliary computing services for ground users. First, the system cost is defined considering the energy consumption of the UAV, the energy consumption of the user, and the delay of the user at the same time. Take into account the dynamic allocation of bandwidth by the user and the dynamic allocation of computational resources by the UAV, the flight trajectory of UAVs, the offloading object and the offloading ratio of users are jointly optimized to minimize the system cost. Due to the dynamic and long-term feature of the problem, it is described as a Markov decision process. A joint computing offloading and trajectory algorithm is proposed based on the PPO in deep reinforcement learning. Simulation results show the convergence of the proposed algorithm. The proposed algorithm has superior performance compared with the benchmark algorithms.