Joint Rate Control and Load-Balancing Routing with QoS Guarantee in LEO Satellite Networks

Qi, Xiaoxin; Zhang, Bing; Qiu, Zhiliang

doi:10.1587/transcom.2020ebp3016

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…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. Considering the traffic distribution density in different areas, the authors in [14] proposed a distributed routing algorithm based on traffic prediction.…”

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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Section: Introductionmentioning

confidence: 99%