We study how radio waves propagate through complex physical environments and develop models that describe wireless channels with both physical insight and empirical validity.

Our research covers a wide range of scenarios, from urban, indoor, and vehicular environments to emerging aerial and non-terrestrial settings, to understand how geometry, materials, mobility, and antenna configurations shape wireless links. 

By combining theory, measurements, and simulation, we build channel models that are useful not only for explaining propagation phenomena but also for supporting the design and development of next-generation wireless systems. 

This line of research provides essential foundations for coverage analysis, interference prediction, digital-twin-based simulation, and the design of future communication networks. 

We develop signal processing and communication algorithms for robust and efficient wireless systems in increasingly challenging environments, informed by channel characteristics and propagation insights.

Our interests include waveform design, channel estimation, beamforming, synchronization, compressed sensing, and sensing-aware signal processing for future wideband and high-frequency wireless links. 

As wireless systems continue to expand toward wider bandwidths, higher frequencies, and more dynamic operating conditions, the role of signal processing becomes even more critical in bridging theoretical system design and practical implementation. 

Through this research, we aim to improve link reliability, spectral efficiency, and system adaptability while providing algorithmic foundations for future wireless and integrated communication platforms. 

We study how to evaluate, predict, and optimize the quality of service of wireless networks using realistic models, simulation frameworks, and data-driven methodologies.

Our research addresses coverage, interference, scalability, and resource management problems in modern and future wireless networks, with an emphasis on understanding and improving network behavior under realistic and dynamic conditions.

To this end, we are interested in stochastic geometry, system-level simulations, digital-twin-based modeling, and AI-assisted optimization approaches that support more reliable network analysis and control. 

This research contributes to the development of practical tools for network planning, performance prediction, and decision-making in evolving wireless environments.