Interference-Aware Emergent Random Access Protocol for Downlink LEO Satellite Networks
CoRR(2024)
摘要
In this article, we propose a multi-agent deep reinforcement learning (MADRL)
framework to train a multiple access protocol for downlink low earth orbit
(LEO) satellite networks. By improving the existing learned protocol, emergent
random access channel (eRACH), our proposed method, coined centralized and
compressed emergent signaling for eRACH (Ce2RACH), can mitigate inter-satellite
interference by exchanging additional signaling messages jointly learned
through the MADRL training process. Simulations demonstrate that Ce2RACH
achieves up to 36.65
cost of signaling messages increase linearly with the number of users.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要