# Energy Efficient UAV Communication with Energy Harvesting

arXiv: https://arxiv.org/abs/1911.00351 · HTML: https://mlsanity.com/paper/1911.00351/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [UAV Communications for Sustainable Federated Learning](https://mlsanity.com/paper/2103.11073/) (2021)
- [E3-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2308.04774/) (2023)
- [Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning](https://mlsanity.com/paper/2106.00845/) (2021)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2309.00017/) (2023)
- [LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks](https://mlsanity.com/paper/2507.17188/) (2025)

## Abstract (arXiv)

This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless communication system with energy harvesting, where the UAV transfers energy to the users in half duplex or full duplex, and the users harvest energy for data transmission to the UAV. We minimize the total energy consumption of the UAV while accomplishing the minimal data transmission requests of the users. The original optimization problem is decomposed into two subproblems: path planning subproblem and energy minimization subproblem with fixed path planning. For path planning subproblem, the optimal visiting order is obtained by using the dual method and the trajectory is optimized via the successive convex approximation technique. For energy minimization subproblem with fixed path planning, we firstly obtain the optimal portion of data transmission time within the entire procedure and the optimal transmission power of each user. Then, the the energy minimization subproblem is greatly simplified and it is efficiently solved via a one-dimensional search method. Simulation results are illustrated to verify the theoretical findings.