# Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1811.05053 · HTML: https://mlsanity.com/paper/1811.05053/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization](https://mlsanity.com/paper/2105.11013/) (2021)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2306.10071/) (2023)
- [Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey](https://mlsanity.com/paper/2508.20315/) (2025)
- [A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2202.10612/) (2022)
- [Mastering Complex Coordination through Attention-based Dynamic Graph](https://mlsanity.com/paper/2312.04245/) (2023)
- [StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigm](https://mlsanity.com/paper/2512.16444/) (2025)

## Abstract (arXiv)

In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operating frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized into two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence.