[ACCOLADES]
Pramod Khargonekar, vice chancellor for research and Distinguished Professor of electrical engineering and computer science, won the 2019 IEEE Control Systems Award for outstanding contributions to robust and optimal control theory. His research contributions span systems and control theory and applications, including foundational contributions to robust and H-infinity optimal control theory. His work has had wide-ranging impact on theoretical developments in the field as well as the emergence of computeraided design tools. Khargonekar also was appointed to the editorial board of the Proceedings of the IEEE, the flagship IEEE journal in the fields of electronics, electrical and computer engineering, and computer science. The publication’s board members are responsible for generating ideas, reviewing manuscripts, helping plan special issues and sections, and offering advice to the editor-in-chief and managing editor. Khargonekar began his three-year term on Jan. 1, 2020.
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UCI Department of Electrical Engineering and Computer Science
The IEEE International Conference on Communications awarded Lee Swindlehurst and two colleagues a 2020 Best Paper Award for their work that details using a type of machine learning to improve performance in multi-antenna wireless communication systems. Swindlehurst, professor of electrical engineering and computer science, collaborated on “SVM-based Channel Estimation and Data Detection for Massive MIMO Systems with OneBit ADCs” with San Diego State professor Duy Nguyen and Ly Van Nugyen, a doctoral student in the UC Irvine/SDSU Joint Program in Computational Science. The paper describes using a classical method of machine learning called support vector machines to target new 5G-and-beyond systems, which employ large arrays of antennas. These arrangements, called massive MIMO – multi-input, multi-output wireless systems – can be costly and use a lot of power, so researchers seek ways to reduce price and power usage without sacrificing performance. “Our paper shows how to use machine learning and signal processing to reduce the performance loss that results when very low-resolution (one-bit) sampling hardware, referred to as analogto-digital converters, are used in a massive MIMO implementation,” said Swindlehurst.
