Antennas and Machine Learning
Dr. Christos Christodoulou
Jim and Ellen King Dean of Engineering and Computing
Abstract: The ever-increasing demand for higher-data rates to sustain high-fidelity video/audio in terrestrial and satellite applications, within a confined volume, have introduced a greater burden in the design of today’s transmitting and receiving antennas. Antennas need to be able to change their operating frequencies, polarizations, and radiation patterns in response to changes in the RF environmental conditions (cognitive radio) or changes in system requirements and operation missions (space communications).
A solution to this problem is the re-configurable antenna controlled by using machine learning algorithms embedded in various types of microprocessors, such as ASICS, FPGAS, Arduinos, etc. These types of software-controlled reconfigurable antennas can provide great versatility in applications such as cognitive radio, MIMO systems, smart antennas, cubesat communications, etc. Furthermore, these antennas, using machine learning, can learn and adapt to variations in their surroundings by deciding to change their transmitter and receiver parameters according to certain predetermined desired responses.
Christos G. Christodoulou received his Ph.D. degree in Electrical Engineering from North Carolina State University in 1985. He is currently the Dean of the School of Engineering and Computing at the University of New Mexico.
He is an IEEE Fellow, a member of Commission B of the U.S. National Committee (USNC) for URSI, and a Distinguished Professor at UNM. He is the recipient of the 2010 IEEE John Krauss Antenna Award for his work on reconfigurable fractal antennas using MEMS switches and has been inducted in the Alumni Hall of Fame for the Electrical and Computer Engineering Department, at North Carolina State University, in 2016.
He was appointed as an IEEE AP-S Distinguished Lecturer (2007-2010) and served as an associate editor for the IEEE Transactions on Antennas and Propagation for six years. He served as a co-editor for a special issue on “Reconfigurable Systems” in the IEEE Proceedings (March 2015), a co-editor of the IEEE Antennas and Propagation Special issue on “Synthesis and Optimization Techniques in Electromagnetics and Antenna System Design” (March 2007), and for the Special issue on “Antenna Systems and Propagation for Cognitive Radio” in 2014. Since 2013 he has been serving as the series editor for Artech House Publishing company for the areas of Antennas, Propagation, and Electromagnetics.
He has published around 600 papers in journals and conferences, written 19 book chapters, co-authored 9 books, and has several patents.