Propagation Model

This research area focuses on extending classic propagation and diffusion models to account for the complex structure of multilayer networks and heterogeneous network topologies. Such incorporation is critical because real-world spreading processes—including information diffusion, epidemics, and behaviors—occur not within isolated networks but across interconnected or multiplex network layers, and network topology (e.g., degree distributions) significantly affects diffusion dynamics. These models aim to better capture dynamical and structural complexities, enabling more accurate predictions and interventions.

This theme encompasses the development, adaptation, and validation of empirical and semi-empirical radio wave propagation models to estimate path loss and signal coverage under diverse conditions, including urban, suburban, and indoor environments and across frequency bands ranging from sub-2 GHz to multi-GHz bands. These models are crucial for network planning, optimization, and design, enabling cost-effective performance prediction without extensive measurements. The research highlights the role of environmental parameters, antenna height, and frequency in modelling signal attenuation, and proposes novel or adapted models with improved accuracy validated against field measurements.

This area investigates the integration of physical principles, partial differential equations, and reaction-diffusion systems to model wave propagation in complex media and the spatial-temporal dynamics of infectious disease spread, including COVID-19. By combining electrical, mechanical, thermal, and stochastic aspects into unified models and using continuum physics and advanced mathematical techniques, researchers aim to capture the multi-dimensional and multi-scale nature of real-world propagation phenomena. Such approaches yield improved understanding and predictive accuracy across disciplines from neuroscience to epidemiology to coastal engineering.