Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad-hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multihop air-to-air links. In this article, we conceive space-air-ground integrated networks for supporting ubiquitous maritime communications, where the low earth orbit satellite constellations, passenger air-planes, terrestrial base stations, ships, serve as the space, air, ground, and sea layer, respectively. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of space-air-ground integrated networks, we propose a deep learning aided multi-objective routing algorithm, which exploits the quasi-predictable network topology and operates in a distributed manner. Our simulation results — based on real satellite, flight, and shipping data in the North Atlantic region — show that the integrated network enhances the coverage quality by reducing the end-to-end delay and by boosting the end-to-end throughput, as well as improving the path-lifetime. The results demonstrate that our deep learning aided multi-objective routing algorithm is capable of achieving near pareto-optimal performance.

中文翻译：

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依赖真实卫星、飞行和航运数据的天-空-地一体化网络的深度学习辅助路由


目前的海上通信主要依靠卫星，传输资源贫乏，因此其性能比现代地面无线网络差。随着跨大陆空中交通的增长，依赖商用客机的航空自组织网络这一有前景的概念有可能通过空对地和多跳空对空链路增强基于卫星的海上通信。在本文中，我们设想了支持无处不在的海上通信的空-空-地一体化网络，其中近地轨道卫星星座、客机、地面基站、船舶作为空间、空中、地面和海洋层， 分别。为了满足异构服务需求，并适应空地一体化网络的时变和自组织性质，我们提出了一种深度学习辅助的多目标路由算法，该算法利用准可预测的网络拓扑并在分布式方式。我们的模拟结果（基于北大西洋地区的真实卫星、飞行和航运数据）表明，集成网络通过减少端到端延迟和提高端到端吞吐量来提高覆盖质量，如下所示以及改善路径寿命。结果表明，我们的深度学习辅助多目标路由算法能够实现接近帕累托最优的性能。