# Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs

arXiv: https://arxiv.org/abs/2404.10845 · HTML: https://mlsanity.com/paper/2404.10845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Cache-enabling UAV Communications: Network Deployment and Resource Allocation](https://mlsanity.com/paper/2007.11501/) (2020)
- [Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks](https://mlsanity.com/paper/2008.05168/) (2020)

## Abstract (arXiv)

This paper presents a Micro-Unmanned Aerial Vehicle (UAV)-enhanced content management system for disaster scenarios where communication infrastructure is generally compromised. Utilizing a hybrid network of stationary and mobile Micro-UAVs, this system aims to provide crucial content access to isolated communities. In the developed architecture, stationary anchor UAVs, equipped with vertical and lateral links, serve users in individual disaster-affected communities. and mobile micro-ferrying UAVs, with enhanced mobility, extend coverage across multiple such communities. The primary goal is to devise a content dissemination system that dynamically learns caching policies to maximize content accessibility to users left without communication infrastructure. The core contribution is an adaptive content dissemination framework that employs a decentralized Top-k Multi-Armed Bandit learning approach for efficient UAV caching decisions. This approach accounts for geo-temporal variations in content popularity and diverse user demands. Additionally, a Selective Caching Algorithm is proposed to minimize redundant content copies by leveraging inter-UAV information sharing. Through functional verification and performance evaluation, the proposed framework demonstrates improved system performance and adaptability across varying network sizes, micro-UAV swarms, and content popularity distributions.