USING STOCHASTIC GEOMETRY AND MACHINE LEARNING FOR DESIGNING AND MANAGING LARGE-SCALE WIRELESS NETWORKS
Designing and managing large-scale wireless networks using stochastic geometry and machine learning are discussed for one intriguing network architecture, which is composed of cloud and fog nodes, and dubbed as cloud-fog-thing network architecture, that is under consideration for 5G. Some important points of this architecture that are the optimum number of fog nodes and their locations are determined in an attempt to increase the average data rate and decrease the transmission delay using stochastic geometry and machine learning. Interestingly, these results may be adapted to increase the understanding of the role of spinal cord plasticity in learning when brain and spinal cord are treated as a cloud and fog network, respectively. This may ultimately suggest new means of treating central nervous system disorders associated with the spinal cord plasticity. Based on this duality, a modified coded caching policy is proposed as well for wireless networks inspired from the central nervous system, i.e., from the cooperation between brain and spinal cord. Lastly, the importance of machine learning in wireless communications and their application areas in large-scale wireless networks are emphasized in this seminar.
Eren Balevi received his B.Sc., M.Sc. and Ph.D degree from the Electrical and Electronics Engineering Department of Middle East Technical University. He worked as an instructor in the Electrical Engineering Department of University of South Florida. Now, he is working as a post-doctoral researcher in the University of Texas, Austin in the wireless networking and communications group.