Damoon Shahbaztabar
Concordia University, Canada
Damoon Shahbaztabar (Member, IEEE) received his Ph.D. degree in Electrical and Computer Engineering from Concordia University, Montréal, Canada, in 2026. He has over 13 years of combined industry and academic experience in wireless communications, radio access networks, and next-generation mobile communication technologies.
He has held senior engineering and research positions at telecommunications companies, including Ericsson, Nokia, Benetel, and Eastlink, focusing on radio access network (RAN) design, integration, performance evaluation, and optimization. He also previously served as a Scientific Researcher at Aalto University, Espoo, Finland.
He is currently a Principal Wireless Engineer at Photon-IV Inc., Canada, where his work focuses on next-generation wireless technologies, Open Radio Access Networks (O-RAN), satellite communications, and integrated terrestrial and non-terrestrial networks (NTNs). He is also a Postdoctoral Research Fellow at Concordia University, where he conducts collaborative research supported by Mitacs, bridging academic research and industrial applications.
His research interests include wireless communications, statistical dependence modeling using copula theory, reconfigurable intelligent surfaces (RISs), satellite communications, non-terrestrial networks, and machine learning for 5G-Advanced and 6G wireless networks.
Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in next-generation wireless networks by enabling programmable control of the wireless propagation environment. However, their performance can be strongly influenced by statistical dependence arising from shared propagation paths and correlated channel components, often compounded by practical impairments such as finite-resolution phase control and hardware-induced phase errors. Accurately characterizing such dependence is particularly important for reliability-oriented performance analysis, as the joint statistical behavior of channel variables, rather than their marginal distributions alone, can significantly affect key performance metrics, including outage probability, ergodic capacity, achievable rate, and diversity performance.
Many existing analytical approaches rely on independence assumptions, conventional linear correlation measures, or approximations of channel distributions based on techniques such as the central limit theorem (CLT), Gamma approximations, and Laguerre-series expansions. While these approaches offer analytical tractability, they may not fully capture complex dependence structures, particularly nonlinear and tail-dependent relationships encountered in RIS-assisted systems with cascaded wireless channels. This motivates the development of more flexible statistical modeling techniques that can account for channel dependence while supporting tractable performance analysis.
In this talk, we present a copula-based analytical framework for dependence modeling, performance analysis, and optimization of RIS-assisted wireless communication systems. By separating marginal channel distributions from their dependence structure, copula theory enables flexible characterization of joint statistical behavior without restricting the analysis to independence or a particular multivariate distribution family. We discuss how different dependence structures influence system performance and illustrate the application of copula-based modeling to the analysis of key performance metrics and the design of RIS-assisted wireless systems. The talk highlights the potential of dependence-aware analysis to provide deeper insights into system reliability and guide performance optimization under realistic propagation conditions.