Heterogeneous networks

Wireless Network Intelligence @ the Edge

Dr. Mehdi Bennis, Associate Professor, Centre for Wireless Communications, University of Oulu

Jul 8, 11:00 - 12:00

B1 L3 R3119

machine learning game theory Heterogeneous networks

In just a few years, breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing, sifting through this data for inference.

Rachad Atat

M.S., Computer, Electrical and Mathematical Sciences and Engineering

Green communications Cooperative communications Heterogeneous networks Cognitive radios

Rachad Atat was a CTL lab member and graduated from KAUST. Then, he joined the KTH Royal Institute of Technology (KTH) as a PhD student. Selected Publications Book Chapters: M. Z. Shakir, R. Atat, and M. -S. Alouini, "Interference suppression capabilities of smart cognitive-femto networks (SCFN),” Chapter in Self-Organization and Green Applications in Cognitive Radio Networks, Al-Dulaimi, A., Cosmas, J., and Mohammed, A. (Eds), IGI Global, 2012. M.S. Thesis: R. Atat, "Cooperative Content Distribution over Wireless Networks for Delay and Energy Efficiency," King Abdullah University of Science