Adaptation, Modeling, and Analysis of PIM – DM in a LEO Satellite Network Environment

2006 ◽  
Author(s):  
V.P. Hubenko ◽  
R.A. Raines ◽  
M.A. Temple ◽  
R.F. Mills ◽  
M.D. Saeger
Keyword(s):  
Satellite Network ◽  
Network Environment ◽  
Modeling And Analysis ◽  
Leo Satellite

scholarly journals Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenyu Na ◽  
Zheng Pan ◽  
Xin Liu ◽  
Zhian Deng ◽  
Zihe Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Low Earth Orbit ◽  
Traffic Prediction ◽  
Satellite Network ◽  
Integrated Networks ◽  
Distributed Routing ◽  
Learning Machine ◽  
Terrestrial Communications ◽  
Leo Satellite

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space-terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time-varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

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