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Current maritime communications mainly rely on satellites having meagertransmission resources, hence suffering from poorer performance than modernterrestrial wireless networks. With the growth of transcontinental air traffic,the promising concept of aeronautical ad hoc networking relying on commercialpassenger airplanes is potentially capable of enhancing satellite-basedmaritime communications via air-to-ground and multi-hop air-to-air links. Inthis article, we conceive space-air-ground integrated networks (SAGINs) forsupporting ubiquitous maritime communications, where the low-earth-orbitsatellite constellations, passenger airplanes, terrestrial base stations,ships, respectively, serve as the space-, air-, ground- and sea-layer. To meetheterogeneous service requirements, and accommodate the time-varying andself-organizing nature of SAGINs, we propose a deep learning (DL) aidedmulti-objective routing algorithm, which exploits the quasi-predictable networktopology and operates in a distributed manner. Our simulation results based onreal satellite, flight, and shipping data in the North Atlantic region showthat the integrated network enhances the coverage quality by reducing theend-to-end (E2E) delay and by boosting the E2E throughput as well as improvingthe path-lifetime. The results demonstrate that our DL-aided multi-objectiverouting algorithm is capable of achieving near Pareto-optimal performance.

Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data