Distributed deep learning for cooperative computation offloading in low earth orbit satellite networks

Low earth orbit (LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the "ubiquitous connection" of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a thre...

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Bibliographic Details
Published in:China communications Vol. 19; no. 4; pp. 230 - 243
Main Authors: Tang, Qingqing, Fei, Zesong, Li, Bin
Format: Journal Article
Language:English
Published: China Institute of Communications 01.04.2022
School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China%School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China
Key Lab of Broadband Wireless Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications,Ministry of Education,Nanjing 210003,China
Subjects:
computation offloading
Computational modeling
deep neural networks
Delays
LEO satellite networks
Low earth orbit satellites
Optimization
Satellites
Servers
Task analysis
LEO satellite networks
computation of-floading
deep neural networks
ISSN:1673-5447
Online Access:Get full text
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Summary:Low earth orbit (LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the "ubiquitous connection" of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading (DDLCCO) algorithm, where multiple parallel deep neural networks (DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.
ISSN:1673-5447
DOI:10.23919/JCC.2022.04.017