Our work in wireless communication covers several aspects, focusing on optimizing network performance and resource utilization. These aspects include spectrum sharing, edge caching, WiFi positioning, and on-device machine learning. A common theme across our studies is the innovative use of mathematical models and techniques, such as stochastic geometry, mean-field game theory, and geometric equations, to address complex problems in wireless communication.
In the realm of spectrum sharing, our goal is to enhance interference management, maintain service quality, and minimize overlapping coverage areas, ensuring efficient use of the available spectrum. We have implemented a demonstration of the CBRS system and are currently installing the Ieum 5G service in our laboratory. Through these testbeds, we not only verify our algorithms via simulation but also demonstrate their effectiveness in real-life situations.
As for edge computing, we concentrate on developing distributed caching algorithms that reduce computational complexity, adapt to user demand dynamics, and minimize redundant cached data, ultimately leading to a more efficient and responsive network. By addressing these challenges, we aim to create robust, reliable, and high-performing wireless communication systems suitable for various applications and environments.