Advancing Wireless Frontiers: From 5G Lab Foundations to Agentic AI in B5G Systems

Talk 1: A Brief Historical Overview of the 5G DIE-UCH Laboratory: Moving from RF to OWC Systems

Abstract: In a joint effort between Universidad de Chile, Subtel, Ericsson,and Entel; the 5G Laboratory started operations in 2020 in the Department of Electrical Engineering. The objective of this initiative was to provide a space dedicated to research, teaching, exploration, and development of programs and applications related to the new 5G network. Later, 5G NR Non-standalone (NSA) came out commercially in Chile; therefore, new efforts started in 2022 to design, implement and validate a low cost 5G OpenRan NR-SA private network achieving an initial data rate of 114 Mbps, latency of 20.91ms, and a communication range of up to 12 meters. Ongoing research is being done to enhance the network with 5G VoNR, handover mechanisms, and design and implementation of cybersecurity algorithms in the RU and DU units. Besides research and experimental validations with RF technologies, we have been working with optical wireless communications systems (OWC), focusing on IoT applications. Coding and channel models for VLC underground environments have been proposed and experimentally validated in the laboratory and in a real copper underground mine. Furthermore, enhanced modulation and diversity schemes have been proposed and experimentally validated using optical cameras as receivers. Ongoing experimental research is being carried out to use photovoltaic panels as optical receivers and in parallel do energy harvesting to make the IoT devices self-sustainable.  

Bio: Dr. Cesar A. Azurdia-Meza received the B.Sc. degree in Electronics Engineering from Universidad del Valle de Guatemala, Guatemala in 2005, and the M.Sc. degree in Electrical Engineering from Linnaeus University, Sweden in 2009. In 2013 he obtained a Ph.D degree in Electronics and Radio Engineering, Kyung Hee University, Republic of Korea. He joined the Department of Electrical Engineering, University of Chile as an Assistant Professor in August 2013, and since August 2021 as an Associate Professor, where he is currently lecturing on wireless and mobile communication systems. Dr. Azurdia has been the Principal Investigator of several national and international research grants. He has served as a Technical Program Committee (TPC) member for multiple conferences, as well as a reviewer in journals such as IEEE Communications Letters, IEEE Transactions on Wireless Communications, Wireless Personal Communications, IEEE Access, IET Communications, EURASIP Journal on Advances in Signal Processing, among others. Dr. Azurdia is an IEEE Communications Society Member, as well as Member of the IEICE Communications Society. His research interests include topics such as Nyquist’s ISI criterion, OFDM-based systems, SC-FDMA, visible light communication systems, vehicular communications, 5G and beyond enabling technologies, and signal processing techniques for communication systems. He is a co-recipient of the 2019 IEEE LATINCOM Best Paper Award, as well as the 2016 IEEE CONESCAPAN Best Paper Award. 

Talk 2: Agentic AI for Hyperparameter Learning of Neural Receivers in B5G using Multi-Agent Reinforcement Learning and Bayesian Methods

Abstract: Alberto Castro Rojas - Short Bio PhD student in Electrical Engineering at the University of Chile (UChile), specializing in mobile communication systems and machine learning applications. Master's degrees in Statistics from the Pontifical Catholic University of Chile (PUC) and Communications Network Engineering from UChile. Diplomas in Statistics (PUC), Computer Security (UChile), and Internetworking (UChile), and Electrical Civil Engineer from UChile. Adjunct instructor at the Department of Electrical Engineering (UChile) since 2002, teaching advanced programming, signal analysis, and statistics. His professional experience spans over two decades in telecommunications and data science, including leadership roles at SQM, Telefónica Chile, and VTR, where he contributed to next-generation networks, national number portability systems, and advanced analytics for industrial optimization. At SQM, he currently leads data science projects and technology development initiatives, applying statistical modeling and machine learning techniques to optimize mining production processes. Research focuses on integrating machine learning methodologies with mobile communication systems, in particular developing neural radio receivers and optimizing communication performance metrics. Interdisciplinary expertise based on theoretical foundations in statistics and electrical engineering and practical applications in telecommunications infrastructure and industrial analytics, next-generation wireless communication research, and B5G/6G network development. Articles in IEEE in the Optimization of Cognitive Radio Networks and Neural Receiver Systems.  

Bio: Mr. Alberto Castro Rojas is PhD student in Electrical Engineering at the University of Chile (UChile), specializing in mobile communication systems and machine learning applications. Master's degrees in Statistics from the Pontifical Catholic University of Chile (PUC) and Communications Network Engineering from UChile. Diplomas in Statistics (PUC), Computer Security (UChile), and Internetworking (UChile), and Electrical Civil Engineer from UChile. Adjunct instructor at the Department of Electrical Engineering (UChile) since 2002, teaching advanced programming, signal analysis, and statistics. His professional experience spans over two decades in telecommunications and data science, including leadership roles at SQM, Telefónica Chile, and VTR, where he contributed to next-generation networks, national number portability systems, and advanced analytics for industrial optimization. At SQM, he currently leads data science projects and technology development initiatives, applying statistical modeling and machine learning techniques to optimize mining production processes. Research focuses on integrating machine learning methodologies with mobile communication systems, in particular developing neural radio receivers and optimizing communication performance metrics. Interdisciplinary expertise based on theoretical foundations in statistics and electrical engineering and practical applications in telecommunications infrastructure and industrial analytics, next-generation wireless communication research, and B5G/6G network development. Articles in IEEE in the Optimization of Cognitive Radio Networks and Neural Receiver Systems.