Beam-Hopping Based Resource Allocation Algorithm in LEO Satellite Network

Liu, Wanying; Tian, Feng; Jiang, Zaiyang; Li, Guotong; Jiang, Quanjiang

doi:10.1007/978-981-13-5937-8_13

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…The composition of the resource management star [10] is shown in Figure 2. The first part is the management of the collection of all available resources in the satellite network, and the second part is the management of the collection of all deployed services in the satellite network, and the standardized storage and monitoring of them.…”

Section: Resource Management Starmentioning

confidence: 99%

Service composition in satellite network based on the self-adaption genetic algorithm

Niu

2023

2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022)

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For the service composition under the multi-resource and multi-service constraints of the satellite network, without considering the problem that service quality and service priority lead to poor quality of service(QoS) and low service priority matching degree, the priority inverse number of service sequence is defined,the approximation is established aiming at the greatest QoS and the smallest priority inverse number of service sequence, and an adaptive genetic algorithm is proposed to solve the model. The algorithm improves the selection mechanism based on elite-reservation and roulette strategy, and designs adaptive crossover and mutation operators. It solves the shortcoming that standard genetic algorithm is easy to fall into local optimum and avoids the loss of optional solution. The simulation results show that the proposed method increases the QoS of service execution by 13.461% and reduces the priority inverse number by 26.77%. It effectively solves the service composition problem with multi-resource and multi-service constraints in satellite network.

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Section: Resource Management Starmentioning

confidence: 99%

Service composition in satellite network based on the self-adaption genetic algorithm

Niu

2023

2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022)

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“…By formulating the joint resources assignment problem as a joint optimization problem and using a DRL approach, Qiu et al [182] proposed a softwaredefined satellite-terrestrial network to jointly manage caching, networking, and computing resources. 13 Space-air-ground integrated networks (SAGINs) [26] .…”

Section: Ai-based Solutionsmentioning

confidence: 99%

Artificial intelligence for satellite communication: A review

Fourati

Alouini

2021

Intell. and Converged Netw.

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Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects has demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

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“…Compared with a GEO Satellite, an LEO satellite has restricted on-board resources and moves quickly. Further, an LEO satellite has a limited energy capacity owing to its small size [192]; as billions of devices need to be served around the world [193], current satellite resource capability can no longer satisfy demand. To address this shortage of satellite communication resources, an efficient resource scheduling scheme to take full use of the limited resources, must be designed.…”

Section: K Energy Managing 1) Definition and Limitationsmentioning

confidence: 99%

Artificial Intelligence for Satellite Communication: A Review

Fourati,

Alouini

2021

Preprint

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Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-ofthe-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

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Beam-Hopping Based Resource Allocation Algorithm in LEO Satellite Network

Cited by 9 publications

References 7 publications

Service composition in satellite network based on the self-adaption genetic algorithm

Service composition in satellite network based on the self-adaption genetic algorithm

Artificial intelligence for satellite communication: A review

Artificial Intelligence for Satellite Communication: A Review

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