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Title: Reinforcement Learning Empowered Massive IoT Access in LEO-based Non-Terrestrial Networks
Low-Earth orbit (LEO) satellite (SAT) networks exhibit ultra-wide coverage under time-varying SAT network topology. Such wide coverage makes the LEO SAT network support the massive IoT, however, such massive access put existing multiple access protocols ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for massive IoT in LEO SAT networks. Not only showing the performance of our proposed approach (see, Table II), but we also discuss the issue of scalability of deep reinforcement learning (DRL) by showing the convergence behavior (see, Table III and IV).  more » « less
Award ID(s):
2008443
PAR ID:
10433250
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
13th International Conference on Information and Communication Technology Convergence (ICTC)
Page Range / eLocation ID:
1347 to 1350
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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