Eli De Poorter

Eli De Poorter is a professor at Ghent University. He received his master degree in Computer Science Engineering from Ghent University, Belgium, in 2006, and his Ph.D. degree in 2011 at the Department of Information Technology at Ghent University. After obtaining his PhD, he received a FWO postdoctoral research grant and he became professor at the same research group in 2015, where he is currently coordinator of several national and international projects. Since 2017, he is also affiliated with the IMEC research institute. His main research interests include IoT, wireless network protocols, wireless testbeds, wireless sensor and ad hoc networks, indoor localization and self-learning networks. He is part of the program committee of several conferences and is the author or co-author of more than 100 papers published in international journals or in the proceedings of international conferences. He combines experimental wireless research with machine learning techniques, with the aim of using machine learning techniques to detect interference, to detect and predict dependencies between system configurations as well as to efficiently characterize and optimize operational wireless networks.


Key publications
  • M Kulin, T Kazaz, I Moerman, E. De Poorter, “End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications, 2018, IEEE Access 6, 18484-18501
  • Shahid, A., Kim, K. S., De Poorter, E., & Moerman, I. (2017). “Self-Organized Energy-Efficient Cross-Layer Optimization for Device to Device Communication in Heterogeneous Cellular Networks”. IEEE Access, 5, 1117-1128
  • M Kulin, C Fortuna, E De Poorter, D Deschrijver, I Moerman, “Data-driven design of intelligent wireless networks: An overview and tutorial”, 2016, SENSORS, 16 (6), 790
  • Mehari, M. T., De Poorter, E., Couckuyt, I., Deschrijver, D., Vermeeren, G., Plets, D. & Moerman, I. (2016). “Efficient identification of a multi-objective pareto front on a wireless experimentation facility. IEEE Transactions on Wireless Communications”, 15(10), 6662-6675
  • M. Rovcanin, E. De Poorter, I. Moerman, P. Demeester, “A reinforcement learning based solution for cognitive network cooperation between co-located, heterogeneous, wireless sensor networks”, Ad Hoc Networks Journal. Vol 17, June 2014
Publication links