Interference-Aware Emergent Random Access Protocol for Downlink LEO
  Satellite Networks
Chang-Yong Lim Jihong Park Jinho Choi Ju-Hyung Lee Daesub Oh Heewook Kim
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% higher network throughput compared to eRACH, while the
cost of signaling messages increase linearly with the number of users.