Machine Learning-Based User Scheduling in Integrated
Satellite-HAPS-Ground Networks
- URL: http://arxiv.org/abs/2205.13958v2
- Date: Tue, 31 May 2022 12:14:33 GMT
- Title: Machine Learning-Based User Scheduling in Integrated
Satellite-HAPS-Ground Networks
- Authors: Shasha Liu, Hayssam Dahrouj, Mohamed-Slim Alouini
- Abstract summary: Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G)
This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
- Score: 82.58968700765783
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Integrated space-air-ground networks promise to offer a valuable solution
space for empowering the sixth generation of communication networks (6G),
particularly in the context of connecting the unconnected and ultraconnecting
the connected. Such digital inclusion thrive makes resource management
problems, especially those accounting for load-balancing considerations, of
particular interest. The conventional model-based optimization methods,
however, often fail to meet the real-time processing and quality-of-service
needs, due to the high heterogeneity of the space-air-ground networks, and the
typical complexity of the classical algorithms. Given the premises of
artificial intelligence at automating wireless networks design, this paper
focuses on showcasing the prospects of machine learning in the context of user
scheduling in integrated space-air-ground communications. The paper first
overviews the most relevant state-of-the art in the context of machine learning
applications to the resource allocation problems, with a dedicated attention to
space-air-ground networks. The paper then proposes, and shows the benefit of,
one specific application that uses ensembling deep neural networks for
optimizing the user scheduling policies in integrated space-high altitude
platform station (HAPS)-ground networks. Finally, the paper sheds light on the
challenges and open issues that promise to spur the integration of machine
learning in space-air-ground networks, namely, online HAPS power adaptation,
learning-based channel sensing, data-driven multi-HAPSs resource management,
and intelligent flying taxis-empowered systems.
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