Abstract

The increasing frequency of social and emergency situations in modern cities has exposed the limitations of traditional cellular networks, which are often designed based on average traffic demands. These networks struggle to handle sudden demand peaks, leading to service blockages and degraded quality of service. To address this issue, the use of Unmanned Aerial Vehicles (UAV) as mobile base stations has been proposed as a temporary solution to expand network capacity during high-demand periods. However, existing traffic models, such as Erlang-B, fail to capture the dynamic entry, exit, and variability of dwelling times associated with UAVs, limiting their accuracy in real-world scenarios. To overcome these challenges, this work proposes the Erlang-U model, which extends classical traffic analysis by incorporating Markov chains and combining Erlang and Hyperexponential distributions to accurately model the heterogeneous and dynamic nature of UAV sojourn times. This novel approach enables both analytical and computational modeling of UAV mobility and dynamic availability, providing a more realistic estimation of blocking probabilities in cellular networks. Simulation results demonstrate that the adaptive deployment of UAVs, guided by the proposed model, can reduce blocking probability by over 25% compared to conventional solutions. These findings highlight the importance of selecting appropriate sojourn time models to optimize network resilience and efficiency in dynamic and high-demand environments.

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