# A QoS-Oriented Trajectory Optimization in Swarming Unmanned-Aerial-Vehicles Communications

arXiv: https://arxiv.org/abs/1904.07988 · HTML: https://mlsanity.com/paper/1904.07988/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2405.18707/) (2024)
- [Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs](https://mlsanity.com/paper/2402.02957/) (2024)
- [Learning Efficient Flocking Control based on Gibbs Random Fields](https://mlsanity.com/paper/2502.02984/) (2025)

## Abstract (arXiv)

This letter aims to present a novel approach for unmanned aerial vehicles (UAV)' path planning with respect to certain quality of service requirements. More specifically, we study the max-min fairness problem in an air-to-ground communication system where multiple UAVs and multiple ground stations exist. We jointly optimize the UAVs trajectories and power allocation as well as the user scheduling. To this end, we propose an effective iterative algorithm that relies on the successive convex approximation and the block coordinate decent techniques.