AI-Driven Adaptive Wireless Coverage for Resilient Communication in Variable Weather with Umbrella Networks

Abstract

Weather-induced challenges such as rain fade, fog, and wind significantly impact wireless communication systems, necessitating innovative solutions to maintain reliable connectivity. This paper presents "Umbrella Networks", a novel AI-driven framework for adaptive wireless coverage under varying weather conditions. Leveraging advanced machine learning models, including supervised regression, unsupervised clustering, and reinforcement learning, the framework dynamically optimizes network parameters such as power control, frequency switching, and beam forming. Case studies highlight the impact of adverse weather on wireless communication and demonstrate how AI techniques mitigate these effects. The integration of this adaptive approach with emerging technologies like 5G, satellite communication, and IoT is discussed, alongside challenges in deployment. Comparative analyses between AI-based and traditional adaptation methods reveal substantial improvements in coverage and resilience. Applications in disaster management, smart cities, and agriculture underscore its transformative potential. This work paves the way for resilient, weather-adaptive wireless communication systems.