Time-Varying Topology Model for Dynamic Routing in LEO Satellite Constellation Networks

Han, Zhenzhen; Xu, Chuan; Zhao, Guofeng; Wang, Shanshan; Cheng, Kefei; Yu, Shui

doi:10.1109/tvt.2022.3217952

Cited by 37 publications

(5 citation statements)

References 31 publications

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“…Different from terrestrial networks, similar to the existing related work in [9,44], the cooperative caching and resource allocation problem is discussed to reduce the problem complexity in a quasi-static scenario. Specifically, a time-varying satellite network is divided into several segments by a division approach based on time slots, where the network topology remains unchanged within each time slot but can vary over different time slots.…”

Section: Cooperative Caching and Resource Allocationmentioning

confidence: 99%

“…For conventional satellite networks with just data routing and forwarding, terminal users need to access internet services from the ground by satellite networks, which can result in high service latency and large network load [9][10][11]. Considering the characteristics of global seamless coverage and low transmission latency for LEO satellite networks, satellite edge caching, as viewed as a new paradigm, can provide edge caching services for terminal users, where caching servers are placed on LEO satellites.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Gao,

Shao,

Wang

et al. 2024

Electronics

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Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided for users by satellite networks, there will be high capital expenditures in terms of communication delay and bandwidth usage. To this end, in this paper, a cooperative-caching and resource-allocation problem is investigated in integrated satellite–terrestrial networks. Popular contents, which are cached on satellites and ground data centers, can be accessed via inter-satellite and satellite–terrestrial networks in a cooperative way. The optimization problem is formulated to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. A cooperative caching and resource allocation (CCRA) algorithm based on a neighborhood search is proposed to address the problem. The simulation results demonstrate that the proposed CCRA algorithm outperforms Greedy and BFS in reducing the deployment costs.

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Section: Cooperative Caching and Resource Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Gao,

Shao,

Wang

et al. 2024

Electronics

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“…It is also worth mentioning that on the basis of the time slice model of satellite networks, there are usually little changes in the visibility matrix between adjacent time slices. There is an adaptive method [19] for the simulated annealing algorithm, which utilizes the solution in the last time slice to reduce redundant computing and iterations.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Topology in Satellite–UAV Collaborative IoT: A Graph Partitioning Simulated Annealing Approach

Zhuo,

Feng,

Yang

et al. 2024

Drones

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Currently, space-based information networks, represented by satellite Internet, are rapidly developing. UAVs can serve as airborne mobile terminals, representing a novel node in satellite IoT, offering more accurate and robust data streaming for connecting global satellite–UAV collaborative IoT systems. It is characterized by high-speed dynamics, with node distances and visibility constantly changing over time. Therefore, there is a need for faster and higher-quality topology optimization research. A reliable, secure, and adaptable network topology optimization algorithm has been proposed to handle various complex scenarios. Additionally, considering the dynamic and time-varying nature of these types of networks, the concept of time slices has been introduced to accelerate the iterative efficiency of problem-solving. Experimental results demonstrate that the proposed algorithm is expected to exhibit better convergence and performance in subsequent iterations compared with traditional solutions. Besides being a solution for topology optimization, the proposed algorithm offers a new way of thinking, enabling the handling of larger satellite–UAV collaborative IoT systems.

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“…Due to the high-speed movement of satellites, users must selectively hand over among candidate satellites to maintain communication continuity, necessitating handover algorithms to minimize communication interruptions and delays. Furthermore, during real-world communication scenarios, the duration of user communication typically exceeds the visibility period of a single satellite, and due to the time-varying characteristics of satellites, it is necessary to analyze the dynamic topology of the satellite network, which varies at different moments [14], leading to differences in the handover diagrams formed by the set of candidate satellites. As the satellite's movement causes the user to move out of the coverage area of the current servicing satellite, ground users must continuously hand over to other visible satellites during the communication process to ensure communication continuity.…”

Section: Introductionmentioning

confidence: 99%

A Graph Reinforcement Learning-Based Handover Strategy for Low Earth Orbit Satellites under Power Grid Scenarios

Yu,

Gao,

Zhang

2024

Aerospace

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Amidst the escalating need for stable power supplies and high-quality communication services in remote regions globally, due to challenges associated with deploying a conventional power communication infrastructure and its susceptibility to natural disasters, LEO satellite networks present a promising solution for broad geographical coverage and the provision of stable and high-speed communication services in remote regions. Given the necessity for frequent handovers to maintain service continuity, due to the high mobility of LEO satellites, a primary technical challenge confronting LEO satellite networks lies in efficiently managing the handover process between satellites, to guarantee the continuity and quality of communication services, particularly for power services. Thus, there is a critical need to explore satellite handover optimization algorithms. This paper presents a handover optimization scheme that integrates deep reinforcement learning (DRL) and graph neural networks (GNN) to dynamically optimize the satellite handover process and adapt to the time-varying satellite network environment. DRL models can effectively detect changes in the topology of satellite handover graphs across different time periods by leveraging the powerful representational capabilities of GNNs to make optimal handover decisions. Simulation experiments confirm that the handover strategy based on the fusion of message-passing neural network and deep Q-network algorithm (MPNN-DQN) outperforms traditional handover mechanisms and DRL-based strategies in reducing handover frequency, lowering communication latency, and achieving network load balancing. Integrating DRL and GNN into the satellite handover mechanism enhances the communication continuity and reliability of power systems in remote areas, while also offering a new direction for the design and optimization of future power system communication networks. This research contributes to the advancement of sophisticated satellite communication architectures that facilitate high-speed and reliable internet access in remote regions worldwide.

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Time-Varying Topology Model for Dynamic Routing in LEO Satellite Constellation Networks

Cited by 37 publications

References 31 publications

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Optimizing Topology in Satellite–UAV Collaborative IoT: A Graph Partitioning Simulated Annealing Approach

A Graph Reinforcement Learning-Based Handover Strategy for Low Earth Orbit Satellites under Power Grid Scenarios

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