Planning and deployment of wireless networks: a data-driven machine learning and optimization framework based on urban mesh and 5G networks

Wireless networks play a fundamental role in the modern world, providing essential infrastructure for applications ranging from industrial automation to smart city development. Different wireless network architectures, such as mobile networks, wireless sensor networks, and wireless mesh networks, are used for different purposes and address specific requirements. Despite their distinct characteristics, these networks share common challenges in planning and deployment, particularly in complex urban environments where factors such as signal propagation, connectivity, and energy consumption must be carefully managed. This thesis addresses these challenges by proposing data-driven approaches based on machine learning and optimization techniques. It aims to fill critical gaps in the literature, particularly regarding accurate signal strength prediction and the optimal placement of relay devices. To achieve these objectives, three interrelated studies are presented in this thesis. In the first study, a machine learn