Load-Balancing Routing Algorithm Based on Segment Routing for Traffic Return in LEO Satellite Networks

Wei, Liu; Tao, Ying; Liu, Liang

doi:10.1109/access.2019.2934932

Cited by 42 publications

(16 citation statements)

References 17 publications

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“…The LEO satellite network topology in the whole inter-satellite communication process is time-varying and redundantly connected multipath may exist, which causes fast retransmit and recovery and delayed acknowledgments. Several cooperative routing algorithms for satellite networks have been proposed to improve the network throughout and end-to-end delay performance [254][255][256][257][258][259]. In UAV group networks [260,261], the mobility and altitude of UAVs, transmitted power, inter-UAV distances, extraneous noise, uneven UAV access users' traffic distribution in both spatial and temporal domains, will largely affect the routing protocol [262] and conventional routing protocols fail to work.…”

Section: Space-air-ground-sea Integrated Networkmentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

1,329

495

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Section: Space-air-ground-sea Integrated Networkmentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

1,329

495

View full text Add to dashboard Cite

show abstract

“…The idea is to represent all users in a certain area by a single node in the center. A common practice is to divide the earth's surface into small grids at equal intervals of latitude and longitude [16]. As a result, there are much more user nodes in polar regions than in equatorial regions, which is contrary to the actual situation.…”

Section: E User Access Modelmentioning

confidence: 99%

Capacity Analysis of LEO Mega-Constellation Networks

et al. 2022

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This paper proposes a complete network capacity analysis framework for Low-Earth-Orbit (LEO) mega-constellations, where a network capacity estimation problem considering the link packet loss rate is formulated with the support of the time-variant network topology model and the task distribution model. The problem is solved in two steps. Firstly, without considering the link packet loss rate, an improved fullypolynomial-time approximation (IFPTA) algorithm is proposed to give a sub-optimal solution of the multicommodity-flow (MCF) problem, in which a simpler definition of a commodity is given and proved to be equivalent to the original problem. Secondly, a Jackson-network-based capacity fallback approach is proposed for controlling link packet loss rate beneath a given threshold. Numerical results illustrate the superiority of the proposed IFPTA algorithm in accuracy and time complexity compared to existing solutions. In addition, the capacity characteristics of mega-constellations are analyzed by utilizing the proposed capacity analysis framework, including the relationship between constellation size and capacity, network capacity bottleneck, and influence of task distributions.INDEX TERMS LEO mega-constellation; network capacity; multi-commodity flow problem; Jackson network. I.

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“…On the other hand, most of the work on satellite routing ignores the impact of user request resources on the performance of satellite network. Liu et al [12] proposed a fragment-based load balancing route scheme to control the traffic of LEO satellite network. Qi et al [13] improved the quality of service (QoS) of users by jointly optimizing the rate and routing in LEO satellite network.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning‐Based Routing Algorithm in Satellite‐Terrestrial Integrated Networks

Yin

Huang

et al. 2021

Wireless Communications and Mobile Computing

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Satellite-terrestrial integrated network (STIN) is an indispensable component of the Next Generation Internet (NGI) due to its wide coverage, high flexibility, and seamless communication services. It uses the part of satellite network to provide communication services to the users who cannot communicate directly in terrestrial network. However, existing satellite routing algorithms ignore the users’ request resources and the states of the satellite network. Therefore, these algorithms cannot effectively manage network resources in routing, leading to the congestion of satellite network in advance. To solve this problem, we model the routing problem in satellite network as a finite-state Markov decision process and formulate it as a combinatorial optimization problem. Then, we put forth a Q-learning-based routing algorithm (QLRA). By maximizing users’ utility, our proposed QLRA algorithm is able to select the optimal paths according to the dynamic characteristics of satellite network. Considering that the convergence speed of QLRA is slow due to the routing loop or ping-pong effect in the process of routing, we propose a split-based speed-up convergence strategy and also design a speed-up Q-learning-based routing algorithm, termed SQLRA. In addition, we update the Q value of each node from back to front in the learning process, which further accelerate the convergence speed of SQLRA. Experimental results show that our improved routing algorithm SQLRA greatly enhances the performance of satellite network in terms of throughput, delay, and bit error rate compared with other routing algorithms.

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Load-Balancing Routing Algorithm Based on Segment Routing for Traffic Return in LEO Satellite Networks

Cited by 42 publications

References 17 publications

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Capacity Analysis of LEO Mega-Constellation Networks

Reinforcement Learning‐Based Routing Algorithm in Satellite‐Terrestrial Integrated Networks

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