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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| Published in: | China communications Vol. 19; no. 4; pp. 230 - 243 |
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| Main Authors: | , , |
| 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 |
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| ISSN: | 1673-5447 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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 offload-ing in the LEO satellite network with a three-tier com-putation architecture by leveraging the vertical coop-eration 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 offload-ing decisions to minimize the total execution delay for ground users subject to the limited battery capac-ity of ground users and the computation capability of each LEO satellite.However,the formulated problem is a large-scale nonlinear integer programming prob-lem as the number of ground users and LEO satel-lites 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)algo-rithm,where multiple parallel deep neural networks(DNNs)are adopted to learn the computation offload-ing strategy dynamically.Simulation results show that the proposed algorithm can achieve near-optimal per-formance with low computational complexity com-pared with other computation offloading strategies. |
| Author | Li, Bin Tang, Qingqing Fei, Zesong |
| AuthorAffiliation | 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 |
| AuthorAffiliation_xml | – name: 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 |
| Author_xml | – sequence: 1 givenname: Qingqing surname: Tang fullname: Tang, Qingqing organization: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China – sequence: 2 givenname: Zesong surname: Fei fullname: Fei, Zesong organization: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China – sequence: 3 givenname: Bin surname: Li fullname: Li, Bin organization: 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 |
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| Publisher | China Institute of Communications 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 |
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| SubjectTerms | computation offloading Computational modeling deep neural networks Delays LEO satellite networks Low earth orbit satellites Optimization Satellites Servers Task analysis |
| Title | Distributed deep learning for cooperative computation offloading in low earth orbit satellite networks |
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