Abstract

As society becomes increasingly dependent on high-speed online services, the demand for robust and reliable wireless connections in densely populated urban areas, such as tower blocks, has become urgent. In these environments, buildings, elevations, blocked sight lines, and scattered reflections scramble radio waves in ways that simple free-space equations cannot foresee. Channel models provide engineers with a virtual test bed to estimate behavior, guiding them toward cost-effective antenna and cable placements. This work presents a hybrid framework that combines field measurements with physics-based simulations to address the unique challenges presented by high-rise skylines. Core inputs include site-specific data on wall losses, window reflexivity, and vertical diffusion between floors, all distilled into a single parametric engine. A modular architecture is added, allowing users to refine settings on the fly as new observations arrive. When compared to live network probes, the approach reduces path-loss errors by up to thirty percent and enhances coverage maps significantly beyond standard defaults. Because it is platform-agnostic, the model can support 5G, Wi-Fi, and narrowband IoT alike, streamlining rollouts for service providers. These results show a promising bridge between academic theory and the on-the-ground realities of multi-story city living. The results provide valuable insights that can inform future work on city-level messaging networks and smart building design.