The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively.

翻译：智能交通系统（ITS）的快速发展导致对多样化网络应用的需求日益增长。然而，传统地面接入网络（TAN）难以满足远程ITS节点（如飞机和船舶）的各种应用需求。相比之下，卫星接入网络（SAN）在覆盖灵活性和可用性方面为TAN提供了补充支持。本研究提出了一种基于SAN的新型ITS数据卸载与计算服务方法。我们利用低地球轨道（LEO）卫星和立方体卫星（CubeSat）作为独立的移动边缘计算（MEC）服务器，调度处理ITS节点生成的数据。为优化不同卫星服务器的卸载任务选择、计算及带宽资源分配，我们构建了一个混合整数非线性规划（MINLP）且为NP-hard的联合时延与租赁价格最小化问题。提出了一种采用注意力机制的协作式多智能体近端策略优化（Co-MAPPO）深度强化学习（DRL）方法，以处理智能卸载决策。同时，将剩余子问题分解为三个独立的资源分配子问题，利用凸优化技术获得其最优闭式解析解。我们进行了大量仿真实验，并将所提方法与基线方案进行对比，分别实现了9.9%、5.2%和4.2%的性能提升。