Channel estimation and characterization of intelligent reconfigurable surfaces-aided wireless communication systems

• Grant number: 22/13901-5
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: April 10, 2023
• End date: April 09, 2024
• Field of knowledge: Engineering - Electrical Engineering - Telecommunications
• Principal Investigator: José Cândido Silveira Santos Filho
• Grantee: Fernando Darío Almeida García
• Supervisor: Flavio du Pin Calmon
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: Harvard University, United States
• Associated to the scholarship: 21/03923-9 - An improved framework for sums of Gaussian-class random variables with applications to radar and communication systems, BP.PD

Abstract

Future wireless networks, namely beyond the fifth generation (B5G) and sixth generation (6G), are required to support massive numbers of users with increasingly demanding spectral efficiency and energy efficiency requirements. Recently, reconfigurable intelligent surfaces (RISs) have attracted considerable interest because of their potential to improve wireless network capacity and coverage by intelligently changing the wireless propagation environment. By smartly tuning the propagation characteristics, RIS is envisioned to transform the propagation space into a smart radio environment to realize the diverse applications of B5G and 6G wireless communications. As such, it has received special attention from both industry and academia. Due to the current practical and theoretical relevance of RIS systems, the characterization and assessment of such systems have become imperative. However, to accomplish this, there are two major obstacles that RIS networks must overcome, namely, channel estimation and channel modeling. In this research project, we expect to tackle the mentioned two issues. More precisely, we intend to examine and propose efficient machine learning algorithms to estimate the characteristics of the propagation medium. Moreover, we aim to fully characterize the sum statistics of the ubiquitous composite fading channel so as to quantify, in an exact and asymptotic manner, the key performance metrics of a RIS-aided wireless communication systems. Finally, we aim to outline a general mathematical framework for the sum of composite random variables. This way, the proposal presented herein is of current relevance in the field of wireless communications. (AU)