# Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems

arXiv: https://arxiv.org/abs/1808.00101 · HTML: https://mlsanity.com/paper/1808.00101/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity](https://mlsanity.com/paper/2308.01562/) (2023)
- [Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2107.11015/) (2021)
- [Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks](https://mlsanity.com/paper/2402.04216/) (2024)
- [Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications](https://mlsanity.com/paper/2303.16737/) (2023)
- [Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2411.14052/) (2024)
- [Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach](https://mlsanity.com/paper/2306.12977/) (2023)
- [Energy-Efficient Power Control for Multiple-Task Split Inference in UAVs: A Tiny Learning-Based Approach](https://mlsanity.com/paper/2401.00445/) (2023)
- [Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking](https://mlsanity.com/paper/2401.12079/) (2024)
- [Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach](https://mlsanity.com/paper/2609.30690/) (2026)

## Abstract (arXiv)

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the three-dimensional (3D) aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an offline resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service (QoS) requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on online algorithm design which only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the offline scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on successive convex approximation. Our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descents again to a lower altitude to reduce the path loss of the communication links to the users it serves.