Routing Algorithm with Virtual Topology Toward to Huge Numbers of LEO Mobile Satellite Network Based on SDN

2017 ◽  
Vol 23 (2) ◽  
pp. 285-300 ◽  
Author(s):  
Min Jia ◽  
Siyu Zhu ◽  
Linfang Wang ◽  
Qing Guo ◽  
Haitao Wang ◽  
...  
Keyword(s):  
Routing Algorithm ◽  
Satellite Network ◽  
Virtual Topology ◽  
Mobile Satellite

scholarly journals Multi-QoS adaptive routing algorithm based on SDN for satellite network

2020 ◽  
Vol 768 ◽  
pp. 052035
Author(s):  
Xiaodong Shi ◽  
Yongjun Li ◽  
Shanghong Zhao ◽  
Weilong Wang
Keyword(s):  
Routing Algorithm ◽  
Adaptive Routing ◽  
Satellite Network

Dynamic Satellite Network Routing Algorithm Based on Link Scheduling

2019 ◽  
pp. 939-948
Author(s):  
Bingbing Zhang ◽  
Siqi Li ◽  
Wenjing Kang ◽  
Bo Li ◽  
Ruofei Ma ◽  
...  
Keyword(s):  
Routing Algorithm ◽  
Network Routing ◽  
Link Scheduling ◽  
Satellite Network

scholarly journals Reinforcement Learning-Based Routing Algorithm in Satellite-Terrestrial Integrated Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yabo Yin ◽  
Chuanghe Huang ◽  
Dong-Fang Wu ◽  
Shidong Huang ◽  
M. Wasim Abbas Ashraf ◽  
...  
Keyword(s):  
Routing Algorithm ◽  
Routing Algorithms ◽  
Convergence Speed ◽  
Satellite Network ◽  
Communication Services ◽  
Integrated Network ◽  
Routing Problem ◽  
Integrated Networks ◽  
Q Learning ◽  
Speed Up

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.

Sign in / Sign up

Export Citation Format

Share Document