Capacity of two-layered satellite networks

Liu, Runzi; Sheng, Min; Lui, King-Shan; Wang, Xijun; Zhou, Di; Wang, Weichao

doi:10.1007/s11276-016-1311-2

Cited by 16 publications

(5 citation statements)

References 21 publications

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“…Since the movement of the constellation is fully predictable, these problems can be solved in advance with a specific optimisation objective in mind. These objectives depend on the target service(s) and can be, as listed in Section 1, to maximize the transport capacity [35], [36] of the constellation or to minimize the E2E latency for a set of specific paths.…”

Section: Frequent Link Establishment and Adaptationmentioning

confidence: 99%

“…In these cases, calculating the transport capacity usually involves assigning flow to all possible paths from the source to the destination, as in the Edmons-Karp algorithm, which has been used to calculate the capacity of constellations with multiple orbital shells [36]. However, this is complicated in dynamic and large networks, so upper bounds based on selecting cuts from the network graph have been used [35]. Yet another hindrance of using the Edmonds-Karp algorithm is that is assumes that ideal mechanisms to redistribute the traffic flows are in place.…”

Section: Frequent Link Establishment and Adaptationmentioning

confidence: 99%

See 1 more Smart Citation

NGSO Constellation Design for Global Connectivity

Leyva-Mayorga¹,

Soret²,

Matthiesen³

et al. 2022

Preprint

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Section: Frequent Link Establishment and Adaptationmentioning

confidence: 99%

Section: Frequent Link Establishment and Adaptationmentioning

confidence: 99%

NGSO Constellation Design for Global Connectivity

Leyva-Mayorga¹,

Soret²,

Matthiesen³

et al. 2022

Preprint

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“…A satellite network includes space satellite groups, ground gateways and ground terminals, shown as in figure 1, that every node can be connected together through inter-satellite and satellite-ground links [18]. The on-board processing, switching and routing technology can be applied to accurately acquire, quickly process and efficiently transmit the information.…”

Section: Traffic Model Of Satellite Networkmentioning

confidence: 99%

A Combined Forecasting Model for Satellite Network Self-Similar Traffic

et al. 2019

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Since satellite network traffic is self-similar and long-range-dependent (LRD), after analyzing current network traffic forecasting models, a satellite network traffic combined forecasting model that is based on the decomposition fruit fly optimization algorithm -extreme learning machine (FOA-ELM) is proposed. This forecasting model decomposes LRD network traffic into multiple short-range dependent (SRD) components via empirical mode decomposition (EMD), applies the FOA-ELM forecasting model to the decomposed high-frequency components, and applies the ELM forecasting model to lowfrequency components. The simulation results show that the forecasting model can improve the forecasting accuracy and forecasting speed, reduce the complexity, and achieve effective and efficient forecasting of satellite network traffic. INDEX TERMS Self-similarity, forecasting model, empirical mode decomposition, fruit fly optimization, extreme learning machine.

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“…In these works the capacity is described as the maximum achievable throughput of the network. Similarly, in [9], [10] the authors study two-layered satellite networks, wherein the capacity is defined as the maximum achievable throughput of the network. In [11], [12], the authors investigate the communication capacity of space-ground networks, which is defined as maximum total amount of data downloaded to the ground per unit time.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Information Capacity of Remote Sensing Satellite Networks

Liu

Yang

et al. 2020

IEEE Access

Self Cite

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With the increasing demand for geological and weather information, remote sensing satellite networks (RSSNs) play increasingly important roles in monitoring our planet. On account of the significant differences between RSSNs and traditional communication networks, the traditional communication capacity which only focus on the performance of data transmission process cannot well capture the service capability of RSSNs. In order to provide efficient guidelines to the deployment of RSSNs, in this paper we study a new capacity indicator, called information capacity, which takes into account the whole service process of RSSNs, including information acquisition, processing, storage, and transmission. Specifically, we firstly propose the formal definition of information capacity. Then, a new graph model called microscopic time-expanded graph (MTEG) is developed, which characterizes the intertwined impact of the observation, computational, storage and transmission resources on the service process of RSSNs. Base on this graph model, a mathematical framework is developed to compute the information capacity. Owing to the NP-completeness of the formulated problem, we decompose it into a flow optimization problem and an arc scheduling problem of the MTEG model, and then propose a Graph-based Information Capacity Solving (GICS) algorithm to efficiently solve the problem. Finally, simulation results highlight the necessity of study the information capacity of RSSNs. INDEX TERMS Remote sensing satellite networks, network capacity, performance analysis. RUNZI LIU (Member, IEEE) received the B.S. degree (Hons.) in telecommunications engineering and the Ph.D. degree in communication and infor

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Capacity of two-layered satellite networks

Cited by 16 publications

References 21 publications

NGSO Constellation Design for Global Connectivity

NGSO Constellation Design for Global Connectivity

A Combined Forecasting Model for Satellite Network Self-Similar Traffic

Exploring the Information Capacity of Remote Sensing Satellite Networks

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