# Learning Emergent Random Access Protocol for LEO Satellite Networks

arXiv: https://arxiv.org/abs/2112.01765 · HTML: https://mlsanity.com/paper/2112.01765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Sample Efficient Actor-Critic with Experience Replay](https://mlsanity.com/paper/1611.01224/) (2016)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Online and Offline Reinforcement Learning by Planning with a Learned Model](https://mlsanity.com/paper/2104.06294/) (2021)
- [Learning values across many orders of magnitude](https://mlsanity.com/paper/1602.07714/) (2016)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access](https://mlsanity.com/paper/2007.09948/) (2020)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [CubeSat Communications: Recent Advances and Future Challenges](https://mlsanity.com/paper/1908.09501/) (2019)
- [Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks](https://mlsanity.com/paper/2005.12521/) (2020)
- [Point-to-Point Communication in Integrated Satellite-Aerial Networks: State-of-the-art and Future Challenges](https://mlsanity.com/paper/2012.06182/) (2020)
- [User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet-of-Things](https://mlsanity.com/paper/2005.09826/) (2020)
- [Throughput Maximization of Mixed FSO/RF UAV-aided Mobile Relaying with a Buffer](https://mlsanity.com/paper/2001.01193/) (2020)

## Built on by (most cited first, 3 of 3)

- [Towards Semantic Communication Protocols: A Probabilistic Logic Perspective](https://mlsanity.com/paper/2207.03920/) (2022)
- [Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimization](https://mlsanity.com/paper/2310.20215/) (2023)
- [Interference-Aware Emergent Random Access Protocol for Downlink LEO Satellite Networks](https://mlsanity.com/paper/2402.02350/) (2024)

## Abstract (arXiv)

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.