Jonathan Aguiar Soares

Jonathan Aguiar Soares, born in Santa Maria, Rio Grande do Sul, Brazil, in February 1991, is an accomplished researcher specializing in telecommunications and electrical engineering. He earned his Ph.D. in Electrical Engineering from the State University of Campinas (UNICAMP) in 2024, after completing a Master's degree at UNICAMP in 2021 and a Bachelor's degree at the Pontifical Catholic University of Rio Grande do Sul (PUCRS) in 2019.In recent years, Jonathan has focused on cutting-edge research in communication systems. His work emphasizes the development and application of machine learning techniques to enhance channel estimation, decoding, and signal processing for MIMO and optical networks, with a particular focus on complex-valued neural networks (CVNNs). His research spans several areas of telecommunicationsincluding advanced wireless systems, optical communication, and 5G/6G networksand has been recognized and cited across academia and industry, especially for his semi-supervised learning methods in joint channel estimation and decoding for massive MIMO systems.Jonathan's research has led to multiple high-impact journal and conference publications, including articles in IEEE Wireless Communications Letters and the Journal of Lightwave Technology. His contributions range from developing phase-transmittance radial basis function neural networks to pioneering soft-failure localization methods that integrate machine learning with software-defined networking. Many of these advancements have practical applications, some of which have been patented and licensed by industry.Notable Works:Journal Contributions: Publications such as Semi-Supervised ML-Based Joint Channel Estimation and Decoding for m-MIMO With Gaussian Inference Learning (2023) and Deep Phase-Transmittance RBF Neural Network for Beamforming With Multiple Users (2022) ) demonstrate Jonathan's ability to bridge theoretical advances with practical implementations.Conference Presentations: Presenting on neural network-based subcarrier-level processing, parameter selection for advanced models, and computational complexities of CVNNs at leading conferences (eg, ICMLCN, LATINCOM) highlights his active engagement with the global research community.His innovative work has also resulted in patents for increasing data transmission rates in optical and wireless systems, as well as for neural network-based channel estimation and decoding. Industry adoption of these patented solutions underscores the real-world impact of his research. With over 180 citations and an h-index of 7 on Google Scholar, Jonathan's contributions are becoming a key reference in the field of CVNNs for communications, reflecting his dedication to advancing both theoretical knowledge and industry practice.Currently, Jonathan is developing hardware-accelerated solutions for next-generation wireless communications using CVNNs on FPGA platforms, enabling real-time training and inference for 5G and beyond. Its focus includes dynamic adaptation to channel conditions and scalable MIMO and beamforming systems with real-time, over-the-air performance.Looking ahead, his research targets AI-driven physical layers for 6G, efficient FPGA-based adaptive systems, and cell-free massive MIMOintegrating wireless and optical networks to enhance scalability and reliability. By supporting the transition to AI-native air interfaces, Jonathan is driving innovation in next-generation communication technologies, producing a significant real-world impact. (Source: Lattes Curriculum)