# Cache-enabling UAV Communications: Network Deployment and Resource Allocation

arXiv: https://arxiv.org/abs/2007.11501 · HTML: https://mlsanity.com/paper/2007.11501/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Multi-Armed Bandit Learning for Content Provisioning in Network of UAVs](https://mlsanity.com/paper/2312.14967/) (2023)
- [Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs](https://mlsanity.com/paper/2404.10845/) (2024)
- [A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation](https://mlsanity.com/paper/2407.00496/) (2024)
- [Towards Federated Multi-Armed Bandit Learning for Content Dissemination using Swarm of UAVs](https://mlsanity.com/paper/2501.09146/) (2025)

## Abstract (arXiv)

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.