  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 grant-free 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.
