Publication Date
Fall 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Navrati Saxena
Second Advisor
Genya Ishigaki
Third Advisor
Yash Bhamare
Keywords
Satellite Networks, Routing, Long Short Term Memory (LSTM), Congestion Control, Load Balancing, Multi-Path Routing
Abstract
Satellite networks play a crucial role in global connectivity today and making efficient routing algorithms is crucial for optimal performance. While existing routing algorithms have made significant progress using machine learning techniques, they often overlook network congestion and multiple path availability. This report introduces an enhanced routing framework that builds upon LSTM-based predictive routing using dynamic congestion modeling and multi-path selection. Our approach introduces a busy state metric that tracks satellite memory utilization, allowing for adaptive path selection based on both distance and current network load. Through simulations using a constellation of 20 satellites, our enhanced algorithm demonstrates significant improvements over baseline LSTM-based routing. Our goal was to reduce the end-to-end delay under high network load conditions and improve network throughput through better load distribution. The results show that considering both congestion and multiple path availability provides more resilient and efficient routing in satellite networks, particularly in scenarios with high traffic volumes.
Recommended Citation
Yoo, Jaesung, "ENHANCED INTER-SATELLITE ROUTING WITH MULTI-PATH SELECTION AND CONGESTION MODELING" (2024). Master's Projects. 1452.
https://scholarworks.sjsu.edu/etd_projects/1452
