# Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks

arXiv: https://arxiv.org/abs/1705.02723 · HTML: https://mlsanity.com/paper/1705.02723/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 30 of 32)

- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2502.09724/) (2025)
- [Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2505.08448/) (2025)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning](https://mlsanity.com/paper/2106.00845/) (2021)
- [UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms](https://mlsanity.com/paper/2211.14931/) (2022)
- [Collaborative Learning with a Drone Orchestrator](https://mlsanity.com/paper/2303.02266/) (2023)
- [Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation](https://mlsanity.com/paper/2412.07428/) (2024)
- [A2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems](https://mlsanity.com/paper/2301.06363/) (2023)
- [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)
- [Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication](https://mlsanity.com/paper/2401.17880/) (2024)
- [Non-iterative Optimization of Trajectory and Radio Resource for Aerial Network](https://mlsanity.com/paper/2405.01314/) (2024)
- [Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2112.10630/) (2021)
- [AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty](https://mlsanity.com/paper/2305.00780/) (2023)
- [Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2309.00017/) (2023)
- [UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning](https://mlsanity.com/paper/2309.16713/) (2023)
- [Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks](https://mlsanity.com/paper/2405.17914/) (2024)
- [Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks](https://mlsanity.com/paper/2604.09028/) (2026)
- [Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2108.11012/) (2021)
- [UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach](https://mlsanity.com/paper/2301.01474/) (2023)
- [Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks](https://mlsanity.com/paper/2307.06842/) (2023)
- [On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2312.07917/) (2023)
- [Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking](https://mlsanity.com/paper/2401.12079/) (2024)
- [Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2508.06863/) (2025)
- [Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration](https://mlsanity.com/paper/2603.03595/) (2026)
- [Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey](https://mlsanity.com/paper/2604.07857/) (2026)
- [UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2605.19290/) (2026)
- [RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling](https://mlsanity.com/paper/2605.29538/) (2026)
- [PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks](https://mlsanity.com/paper/2607.17922/) (2026)
- [Arm2Air: Cross-Embodiment Skeleton Transfer for 3D Relay Formation](https://mlsanity.com/paper/2607.27627/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations (BSs) are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAVs' trajectory and power control. The formulated problem is a mixed integer non-convex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the non-convex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge to at least a locally optimal solution. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.