Learning Emergent Random Access Protocol for LEO Satellite Networks

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LEO SAT networks exhibit extremely long link distances of many users under time-varying SAT network topology. This makes existing multiple access protocols, such as random access channel (RACH) based cellular protocol designed for fixed terrestrial network topology, ill-suited. To overcome this issue, in this paper, we propose a novel contention-based random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH). In stark contrast to existing model-based and standardized protocols, eRACH is a model-free approach that emerges through interaction with the non-stationary network environment, using multi-agent deep reinforcement learning (MADRL). Furthermore, by exploiting known SAT orbiting patterns, eRACH does not require central coordination or additional communication across users, while training convergence is stabilized through the regular orbiting patterns. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput with around two times lower average access delay while achieving 0.989 Jain's fairness index.

Automated summary

This paper proposes a novel contention-based random access solution for low Earth orbit satellite (LEO SAT) networks, called eRACH, which achieves automatic protocol establishment through multi-agent deep reinforcement learning in a non-stationary network environment. In contrast to existing model-based and standardized protocols, eRACH does not require central coordination or additional communication across users, and training convergence is stabilized through regular orbiting patterns. Compared to RACH, simulation results show that eRACH achieves 54.6% higher average network throughput, around two times lower average access delay, and a Jain's fairness index of 0.989.