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Deep learning aided routing for space-air-ground integrated networks relying on real satellite, flight, and shipping data

Deep learning aided routing for space-air-ground integrated networks relying on real satellite, flight, and shipping data
Deep learning aided routing for space-air-ground integrated networks relying on real satellite, flight, and shipping data
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-toground and multi-hop air-to-air links. In this article, we conceive space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earthorbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-, groundand sea-layer. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of SAGINs, we propose a deep learning (DL) 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 (E2E) delay and by boosting the E2E throughput as well as improving the path-lifetime. The results demonstrate that our DL-aided multiobjective routing algorithm is capable of achieving near Paretooptimal performance.
1536-1284
Liu, Dong
889643f2-afeb-4479-bd41-3ccedd53d89d
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Cui, Jingjing
dbe3c3ed-762f-4abf-bd7b-8d2737f2f0fc
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Liu, Dong
889643f2-afeb-4479-bd41-3ccedd53d89d
Zhang, Jiankang
6add829f-d955-40ca-8214-27a039defc8a
Cui, Jingjing
dbe3c3ed-762f-4abf-bd7b-8d2737f2f0fc
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Liu, Dong, Zhang, Jiankang, Cui, Jingjing, Ng, Soon Xin, Maunder, Robert and Hanzo, Lajos (2021) Deep learning aided routing for space-air-ground integrated networks relying on real satellite, flight, and shipping data. IEEE Wireless Communications.

Record type: Article

Abstract

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-toground and multi-hop air-to-air links. In this article, we conceive space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earthorbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-, groundand sea-layer. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of SAGINs, we propose a deep learning (DL) 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 (E2E) delay and by boosting the E2E throughput as well as improving the path-lifetime. The results demonstrate that our DL-aided multiobjective routing algorithm is capable of achieving near Paretooptimal performance.

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Published date: 26 October 2021

Identifiers

Local EPrints ID: 452595
URI: http://eprints.soton.ac.uk/id/eprint/452595
ISSN: 1536-1284
PURE UUID: 152a0704-4dc7-4ca2-9dde-5b2ea2302019
ORCID for Dong Liu: ORCID iD orcid.org/0000-0002-0619-1480
ORCID for Jiankang Zhang: ORCID iD orcid.org/0000-0001-5316-1711
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194
ORCID for Robert Maunder: ORCID iD orcid.org/0000-0002-7944-2615
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 11 Dec 2021 11:28
Last modified: 17 Jun 2022 01:40

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Contributors

Author: Dong Liu ORCID iD
Author: Jiankang Zhang ORCID iD
Author: Jingjing Cui
Author: Soon Xin Ng ORCID iD
Author: Robert Maunder ORCID iD
Author: Lajos Hanzo ORCID iD

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