Intelligent wireless communications and high-accuracy positioning systems

January 2021 – October/December 2022

Objective
It indeed consists of two sub-projects. Firstly, as the most promising technology in achieving 10 Gbs peak data rates, millimetre-wave (mmWave) communications have received remarkable attention from academia and industry. Thus, in the project of intelligent wireless communications, we aim to develop several machine learning-based beam tracking algorithms for mobile mmWave communications, which can work efficiently without relying on a priori knowledge of channel dynamics. While in the project of high-accuracy positioning systems, we aim to leverage mmWave signals and other techniques, such as intelligent reflecting surfaces, to achieve centimetre-level localization accuracy.

Background
Driven by the ever-increasing mobile data traffic, 5G-and-beyond (B5G) networks are envisioned as a key enabler to support a variety of novel use cases, such as autonomous cars, industrial automation, multisensory extended reality (XR), e-health, etc. Considering the emergence of these use cases and the more and more complicated network structure, artificial intelligence is expected to be essential to assist in making the B5G version conceivable.

With regard to high-accuracy localization, it will play a critical role in almost all use cases of the B5G networks. Specifically, depending on the usage scenarios, the requirement for localization accuracy ranges from 1 cm to 10 cm for smart factory applications. However, most current localization services can, at best, achieve meter-level localization accuracy and, therefore, cannot meet the centimetre-level localization accuracy requirements of the emerging use cases in the B5G era, which emphasizes the need for more advanced localization techniques.

About the Digital Futures Postdoc Fellow
Deyou Zhang is a Digital Futures Postdoc at the School of Electrical Engineering and Computer Science of KTH, supervised by Dr Ming Xiao, Prof. Lihui Wang, and Dr Zhibo Pang. Before joining KTH, he obtained his PhD at the University of Sydney, Australia. His research interests include millimetre-wave communications, intelligent reflecting surfaces, and wireless federated learning.

Main supervisor
Ming Xiao, Associate Professor, Division of ISE, EECS School, KTH

Co-supervisors
Zhibo Pang, Senior Principal Scientist, Department of Automation Technology, ABB Corporate Research Sweden and Adjunct Professor, Department of Intelligent Systems, EECS, KTH

Lihui Wang, Professor and Chair of Sustainable Manufacturing, KTH

Watch the recorded presentation at Digitalize in Stockholm 2022 event: