Murat Yuksel, a professor at UCF’s Department of Electrical and Computer Engineering, is conducting research funded by the U.S. Air Force Research to improve the quality of high frequency wireless networks using machine learning. The project, titled “INWADE,” aims to optimize wireless signal quality in environments where high frequency signals are easily blocked or attenuated. By training deep neural networks to design communication blocks at both the transmitter and receiver, the project seeks to enhance data transfer rates for wireless network users.
The research focuses on utilizing deep learning to fine-tune millimeter-wave signals in the “super-6 GHz” bands, which are difficult to access and maintain due to their sensitivity to environmental factors. Initial findings have shown promising results in solving fundamental link level problems, such as detecting signal direction and arrival angles. The project also aims to demonstrate the feasibility of using deep learning at both the link and network levels to optimize wireless signals.
Yuksel acknowledges the contributions of his students and collaborators in the success of the research, highlighting the importance of collaboration with institutions like Clemson University. Moving forward, the project will explore the co-existence of multiple machine learning agents in wireless networks to improve performance without conflicting goals.
Overall, Yuksel’s research aims to advance the field of wireless networking by leveraging machine learning to address challenges in optimizing high frequency wireless signals and sharing the radio spectrum more efficiently. The potential impact of this work is significant, as it could lead to better utilization of wireless resources and improved wireless connectivity for all users.
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