  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.
