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.

翻译：本文提出了一种基于多智能体深度强化学习（MADRL）框架的训练方法，用于设计下行低轨卫星网络的多址接入协议。通过改进现有的学习协议——涌现随机接入信道（eRACH），我们提出的方法（称为集中式压缩涌现信令的eRACH，Ce2RACH）能够通过交换经MADRL训练过程联合学习的额外信令消息来缓解星间干扰。仿真结果表明，与eRACH相比，Ce2RACH的网络吞吐量提升最高达36.65%，而信令消息的开销随用户数量线性增长。