Learning-Aided Realtime Performance Optimisation of Cognitive UAV-Assisted Disaster Communication

In this work, we propose efficient optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real-time optimisation in embedded UAV-CRN communication invoked for recovering wireless communication services. Particularly, by conceiving advanced optimisation techniques and training deep neural networks, our solutions become capable of supporting real-time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real-time optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.