Abstract:
Ground-based communication infrastructure is often damaged by natural disasters, disrupting the network connectivity, making it diffcult for effcient rescue and relief operations. In such scenarios, unmanned aerial vehicles (UAVs) can serve as aerial base stations to provide emergency network services. In this study, resource management of UAVs in disaster scenarios using 6G Network is investigated by providing a keen insight into effcient channel and bandwidth allocation to maximize the data rates. The three assignment algorithms, i.e., the Hungarian, Greedy, and Random, along with an artificial intelligence-based intra-band carrier aggregation (IBCA) approach, were analyzed. The results depicted that Hungarian provides globally optimal results when it comes to channel assignment and maximum data rates over fxed bandwidth, outperforming the other two algorithms. To address dynamic and heterogeneous bandwidth demands, IBCA was employed. The suggested AI-based model estimates the additional bandwidth required by the user and dynamically allocates available contiguous bandwidth chunks to satisfy heterogeneous users’ data rate requirements. The proposed AI-based IBCA model outperforms traditional scheduling algorithms in terms of data rates, bandwidth, and channel allocation. It also emphasizes the potential of UAVs and emerging 6G technologies in enabling resilient communication systems.
