Very few available individual bandwidth reservation schemes provide efficient and cost-effective bandwidth reservation that is required for safety-critical and time-sensitive vehicular networked applications. These schemes allow vehicles to make reservation requests for the required resources. Accordingly, a Mobile Network Operator (MNO) can allocate and guarantee bandwidth resources based on these requests. However, due to uncertainty in future reservation time and bandwidth costs, the design of an optimized reservation strategy is challenging. In this article, we propose a novel multi-objective bandwidth reservation update approach with an optimal strategy based on Double Deep Q-Network (DDQN). The key design objectives are to minimize the reservation cost with multiple MNOs and to ensure reliable resource provisioning in uncertain situations by solving scenarios such as underbooked and overbooked reservations along the driving path. The enhancements and advantages of our proposed strategy have been demonstrated through extensive experimental results when compared to other methods like greedy update or other deep reinforcement learning approaches. Our strategy demonstrates a 40% reduction in bandwidth costs across all investigated scenarios and simultaneously resolves uncertain situations in a cost-effective manner.

翻译：目前可用的个体带宽预留方案极少能为安全关键和时间敏感的车载网络应用提供所需的高效且经济有效的带宽预留。这些方案允许车辆为所需资源提出预留请求。相应地，移动网络运营商（MNO）可以根据这些请求分配并保障带宽资源。然而，由于未来预留时间和带宽成本的不确定性，设计优化的预留策略具有挑战性。本文提出了一种新颖的多目标带宽预留更新方法，其基于双深度Q网络（DDQN）的最优策略。关键设计目标是通过解决沿行驶路径的预留不足和超额预订等场景，在多个MNO之间最小化预留成本，并确保在不确定情况下的可靠资源供给。通过与其他方法（如贪婪更新或其他深度强化学习方法）进行比较，大量实验结果证明了我们提出的策略的增强效果和优势。我们的策略在所有研究场景中实现了40%的带宽成本降低，同时以经济有效的方式解决了不确定情况。