Learning Emergent Random Access Protocol for LEO Satellite Networks
Ju-Hyung Lee, Hyowoon Seo, Jihong Park, Mehdi Bennis and, Young-Chai Ko

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…
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Taxonomy
TopicsSatellite Communication Systems · Age of Information Optimization
