Learning Emergent Random Access Protocol for LEO Satellite Networks
This addresses the challenge of providing efficient global coverage for users in beyond 5G cellular systems with LEO satellites, representing a novel domain-specific advancement rather than an incremental improvement.
The paper tackles the problem of inefficient multiple access in LEO satellite networks due to long distances and time-varying topology by proposing a model-free, grant-free random access protocol using multi-agent deep reinforcement learning, resulting in 54.6% higher average network throughput, around two times lower average access delay, and 0.989 Jain's fairness index compared to existing methods.
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.