Due to the surging increase in data traffic demand, operators try to enhance the coverage and network capacity by intensive spatial reuse. The limiting factor in networks with high spatial reuse is aggregate network interference, which depends strongly on the locations of the interferers. Stochastic geometry allows to make statistical statements about the network performance by systematically incorporating the spatial statistics. The potential of stochastic geometry will be illustrated by means of practical examples related to the energy efficiency in wireless networks, interference management techniques, distributed signal processing, and network access control. Some open problems on the dynamical performance of complex heterogeneous networks will be discussed. Aside from the network performance issues addressed above, the sheer size and complexity of present-day networks makes centralized control and signal processing unviable. The last part of the talk will cover connectivity in large-scale networks and will demonstrate different methods to achieve network control.
About Matthias Wildemeersch
Matthias Wildemeersch is a Postdoctoral Research Scholar at the International Institute for Applied Systems Analysis, Laxenburg, Austria. He received the Ph.D. degree in Electrical Engineering, Mathematics and Computer Science at the University of Twente, the Netherlands, in 2013. Previously, he obtained the M.Sc. degree in electro-mechanical engineering from Ghent University, Belgium. He gained professional experience at the Joint Research Centre of the European Commission, the Agency for Science, Technology and Research (A*STAR) in Singapore, and the Singapore University of Technology and Design (SUTD). Matthias’ research interests span various aspects of probability theory, signal processing, and control, applied to wireless networks and network science. His current research focuses on the dynamical behavior of large-scale networks, and aims to evaluate and improve network resilience.
This event will be conducted in English