Synergistic Urban Morphometry for Planning and Sensing mmWave Wireless Networks

Millimeter-wave (mmWave) communication, pivotal to 5G and future 6G networks, offers multi-gigabit data rates but faces formidable deployment challenges in dense urban environments due to high path loss, susceptibility to blockages, and complex multipath dynamics. This thesis addresses these challenges by developing a synergistic framework that integrates high-fidelity geospatial modeling with advanced analytical techniques to optimize mmWave network design and environmental awareness in urban street canyon environments. At its core, the research combines detailed 3D ray-tracing simulations—enriched with real-world geospatial data on building geometries, materials, and terrain—with a novel manifold-theoretic model that abstracts the urban propagation environment as a smooth manifold. This abstraction captures transitions between line-of-sight (LOS), reflection, diffraction, and diffuse scattering mechanisms, providing both predictive accuracy and analytical scalability. Key contributions include: (1) the development of a GIS-based simulation platform, validated using downtown Toronto as a case study, which revealed the critical role of diffuse scattering in enabling non-line-of-sight (NLOS) connectivity; (2) the introduction of an omnidirectional cylindrical receiver grid enabling rich spatiotemporal sampling of multipath components, thus enhancing environmental sensing capabilities; and (3) the derivation of a Height-Weighted Morphology Utility Function (HWM-UF) that identifies an optimal base station height of approximately 50 ± 3 meters for urban mmWave deployment, balancing signal coverage and link stability. The thesis further demonstrates that mmWave infrastructure, when designed with this integrated framework, can serve as dual-use systems—delivering high-speed communication while functioning as distributed sensors for real-time environmental monitoring. Experimental validations and case studies confirm improved coverage prediction accuracy and robustness against dynamic obstructions such as vehicles and pedestrians. By bridging site-specific simulation with theoretical innovation, this work advances the state-of-the-art in urban mmWave network design. It lays the foundation for intelligent urban ecosystems where communication and sensing converge, enhancing safety, efficiency, and connectivity in next-generation smart cities. Future directions include integrating real-time geospatial data, adaptive traffic-aware modeling, and extending the framework to sub-THz propagation environments.

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