# Intelligent Spectrum Sharing in Integrated TN-NTNs: A Hierarchical Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2503.06720 · HTML: https://mlsanity.com/paper/2503.06720/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 2 of 2)

- [Hierarchical Cognitive Spectrum Sharing in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2312.08097/) (2023)
- [Coordinated Dynamic Spectrum Sharing Between Terrestrial and Non-Terrestrial Networks in 5G and Beyond](https://mlsanity.com/paper/2304.12112/) (2023)

## 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)
- [Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks](https://mlsanity.com/paper/2508.19495/) (2025)

## Abstract (arXiv)

Integrating non-terrestrial networks (NTNs) with terrestrial networks (TNs) is key to enhancing coverage, capacity, and reliability in future wireless communications. However, the multi-tier, heterogeneous architecture of these integrated TN-NTNs introduces complex challenges in spectrum sharing and interference management. Conventional optimization approaches struggle to handle the high-dimensional decision space and dynamic nature of these networks. This paper proposes a novel hierarchical deep reinforcement learning (HDRL) framework to address these challenges and enable intelligent spectrum sharing. The proposed framework leverages the inherent hierarchy of the network, with separate policies for each tier, to learn and optimize spectrum allocation decisions at different timescales and levels of abstraction. By decomposing the complex spectrum sharing problem into manageable sub-tasks and allowing for efficient coordination among the tiers, the HDRL approach offers a scalable and adaptive solution for spectrum management in future TN-NTNs. Simulation results demonstrate the superior performance of the proposed framework compared to traditional approaches, highlighting its potential to enhance spectral efficiency and network capacity in dynamic, multi-tier environments.