Learning Emergent Random Access Protocol for LEO Satellite Networks

TL;DR

This paper introduces eRACH, a novel model-free, multi-agent deep reinforcement learning-based random access protocol for LEO satellite networks, significantly improving throughput and reducing access delay without central coordination.

Contribution

The paper presents eRACH, a new emergent, model-free random access protocol for LEO satellites that leverages MADRL and satellite orbit patterns, unlike existing fixed-model protocols.

Findings

eRACH achieves 54.6% higher throughput than RACH.

eRACH reduces average access delay by about half.

eRACH maintains high fairness with a Jain's index of 0.989.

Abstract

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.