# Distributed Coordination for Heterogeneous Non-Terrestrial Networks

arXiv: https://arxiv.org/abs/2502.17366 · HTML: https://mlsanity.com/paper/2502.17366/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

## Builds on (most cited first, 5 of 5)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Triplet-Based Wireless Channel Charting: Architecture and Experiments](https://mlsanity.com/paper/2005.12242/) (2020)
- [Goal-Oriented Semantic Communications for 6G Networks](https://mlsanity.com/paper/2210.09372/) (2022)
- [Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2409.01092/) (2024)
- [Deep Learning Driven Buffer-Aided Cooperative Networks for B5G/6G: Challenges, Solutions, and Future Opportunities](https://mlsanity.com/paper/2401.01195/) (2024)

## Built on by (most cited first, 2 of 2)

- [AI-Native Open RAN for Non-Terrestrial Networks: An Overview](https://mlsanity.com/paper/2507.11935/) (2025)
- [Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks](https://mlsanity.com/paper/2608.13394/) (2026)

## Abstract (arXiv)

To achieve global coverage and ubiquitous connectivity, the non-terrestrial network (NTN) has been regarded as a key enabler in the sixth generation (6G) network, which includes uncrewed aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites. Since the unique characteristics of various NTN platforms strongly affect their implementation and lead to a highly dynamic and heterogeneous NTN scenario, achieving distributed coordination remains an important research direction. However, the explicit and systematic analysis of the individual layers' challenges and corresponding distributed coordination solutions in heterogeneous NTNs has not been proposed yet. Therefore, in this paper, we summarize the unique characteristics of each NTN platform, identify communication challenges within individual layers, and propose potential delay-tolerant or delay-sensitive coordinated solutions accordingly. We further analyse the feasibility of leveraging multi-agent deep reinforcement learning (MADRL) algorithms to achieve the proposed coordinated solutions. Finally, we present a case study of the joint scheduling and trajectory optimization problem in heterogeneous NTN, where a two-timescale multi-agent deep deterministic policy gradient (TTS-MADDPG) algorithm is developed to validate the effectiveness of distributed coordination.