WiseNET aims to enable reliable, energy-efficient connectivity and sensing through the integration of satellite and terrestrial networks. By combining wireless sensing, artificial intelligence, and digital twins, the project will develop methods to understand the radio environment, optimize network resources, and support connected and autonomous systems. WiseNET pursues four scientific objectives:
Develop a digital twin of the radio environment, capturing variations across space, time, and frequency. Driven by collaborative wireless sensing, the model will combine accurate radio-environment predictions with computational efficiency.
Enable AI-driven management of radio and network resources, using an enhanced understanding of the radio environment to coordinate integrated satellite and terrestrial networks, improve spectrum utilization, and reduce end-device power consumption.
Design predictive link adaptation and seamless handover algorithms that anticipate changes in propagation and interference. By jointly adapting transmission settings and network connections, WiseNET aims to maintain reliable communication as devices move and network conditions change.
Develop joint communication and sensing methods for localization, tracking, and collision avoidance in dense deployments of unmanned vehicles. These methods will support robust positioning and situational awareness, complementing satellite navigation in challenging environments.