Many-to-Many Matching-Theory-Based Dynamic Bandwidth Allocation for UAVs

The efficient and reliable cooperation of unmanned aerial vehicles (UAVs) is crucial for the UAV-enabled Internet-of-Things (IoT) services. However, one utmost challenge is how to effectively solve the many-to-many bandwidth allocation problem between UAVs and users (UEs) in a highly dynamic network, where the uncertain Non-Line-of-Sight (NLoS) links and the UE's mobility can seriously impair the stability of network topology. In this article, a task-driven dynamic multiconnectivity matching game framework with multiple service requirements is proposed to maximize the system throughput while ensuring the UEs' delay requirement. A three-layers auction-based dynamic many-to-many full matching algorithm is proposed to achieve the global network bandwidth resource optimization and update all UEs' channel access strategies. A simplified matching algorithm is proposed to achieve the efficient local resource exchange and the dynamic matching quota adjustment between unstable single connectivity (SC) UEs and multiple connectivity (MC) UEs, which can achieve the suboptimal with a lower complexity solution in contrast to the full matching algorithm. Both the full matching and simplified matching algorithms are proved theoretically to achieve the stable solutions. Simulation results show that the system throughput of both proposed algorithms can improve 55% and 38% compared with that of the conventional many-to-one matching, respectively. Both proposed algorithms have the lower complexity than the conventional centralized optimization algorithm, and the complexity of simplified matching algorithm is only 40% of the full matching algorithm. Moreover, the UE's satisfaction is increased by 28% compared with the case without considering the delay factor into the utility function.