A Machine Learning UAV Deployment Approach for Emergency Cellular Networks

This paper proposes a scheme for rapidly deploying a UAV-enabled emergency cellular network (UECN) in disaster scenarios, such as earthquakes or floods, to support rescue operations. The unsupervised placement of UAV aerial base stations (ABSs) is achieved through machine learning (ML) techniques. Specifically, the k-medoids algorithm is utilized to cluster ground users in the disaster area and determine the minimum number of ABSs and their position. ABS's altitude is defined based on its capacity capabilities and propagation environment. The UECN cooperates with undamaged cellular infrastructure via joint coordinated multipoint transmission and reception (CoMP) with neighbouring functional terrestrial macrocell base stations (TBSs) to improve end-user signal quality. Finally, the proposed scheme is comparatively evaluated through simulation, and the results demonstrate its efficiency even in the presence of users and ABSs positioning errors.