Kien Vu (Trung Kien Vu) received the B.Eng. degree from the School of Electronics and Telecommunications, Hanoi University of Science and Technology, Vietnam, in 2012, and the M.Sc. degree in electrical engineering from the School of Electrical Engineering, University of Ulsan, South Korea, in 2014.
From 2015 to 2018, he pursued the D.Sc. degree with the Centre for Wireless Communications (CWC), University of Oulu, Finland. He was a Visitor at Tsinghua University, Beijing, China from Dec. 2018 to Mar. 2019. His research interests include
Kien Vu received the Nokia Foundation grant, the Tekniikan edistanmissäätiö grant, the 2016 European Wireless Best Paper Award, and the Brain Korean Scholarship for his Master studies. He serves as a Reviewer for several major IEEE Transactions and Conferences.
S1: This is a matlab code package, which is related to our articles: "Path selection and rate allocation in self-backhauled mmWave networks", Proc. IEEE Wireless Commun. Netw. Conf., pp. 2371-2376, 15-18 April 2018, Barcelona, Spain, and "Ultra-reliable communication in 5G mmWave networks: A risk-sensitive approach." IEEE Commun. Lett., vol. 22, no. 4, pp. 708-711, 2018.
In this research, we study the main 5G technologies, concerning higher frequency bands, large antenna array and dense small cells, to support new and diverse use-case scenarios and applications for wireless networks.
We propose a new system design, integrated in-band access and backhaul architecture, which jointly schedules a large number of users and provides in-band wireless backhaul to a dense deployment of small cells.
Our objective is to achieve extremely high data rate, low-latency with a reliability guarantee in the presence of network dynamics and scalability.
Most of previous work focus on addressing one or few issues or technologies to tackle the 5G challenges; thus far, to our best knowledge, we are the first to study the problem of how to optimize overall network performance to obtain these predefined demands, while taking backhaul dynamics, traffic load, mmWave channel state into account.
Moreover, we exploit multiple antennas and multiple connectivity techniques to further increase the fast-reliable-high-speed communication in both single hop and multihop wireless backhauls in 5G mmWave networks.
By applying advanced signal processing techniques, mathematical optimization frameworks, and machine learning tools, the research provides important solutions to establish key tradeoffs, such
as between capacity and latency, and between reliability and network density/traffic load.
Moreover, the research results follow the project scheme, which are expected to have significant influences on the development of future wireless networks and 5G radio access systems. Finally, the research results are submitted to the highly scientific communities for publications.
This research visit is partly funded by the UniOGS travel grant and the Academy of Finland 6Genesis Flagship (grant 318927).