# RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.02889 · HTML: https://mlsanity.com/paper/2108.02889/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications](https://mlsanity.com/paper/2002.12271/) (2020)
- [Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces](https://mlsanity.com/paper/1912.01497/) (2019)
- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks](https://mlsanity.com/paper/2010.02749/) (2020)
- [Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2005.00269/) (2020)
- [Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2007.08380/) (2020)
- [Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning](https://mlsanity.com/paper/2105.14142/) (2021)
- [Reconfigurable Intelligent Surfaces with Reflection Pattern Modulation: Beamforming Design and Performance Analysis](https://mlsanity.com/paper/2008.02555/) (2020)
- [Reconfigurable Intelligent Surface-Assisted Aerial-Terrestrial Communications via Multi-Task Learning](https://mlsanity.com/paper/2104.06758/) (2021)
- [Reconfigurable Intelligent Surface assisted Two-Way Communications: Performance Analysis and Optimization](https://mlsanity.com/paper/2001.07907/) (2020)
- [Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer](https://mlsanity.com/paper/2006.01696/) (2020)
- [Reconfigurable Intelligent Surface Assisted Device-to-Device Communications](https://mlsanity.com/paper/2007.00859/) (2020)
- [Common Throughput Maximization for UAV-Enabled Interference Channel with Wireless Powered Communications](https://mlsanity.com/paper/1910.04403/) (2019)

## Built on by (most cited first, 2 of 2)

- [Deep Learning in Wireless Communication Receiver: A Survey](https://mlsanity.com/paper/2501.17184/) (2025)
- [Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications](https://mlsanity.com/paper/2312.04786/) (2023)

## Abstract (arXiv)

Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and thus supplying energy while maintaining seamless connectivity for IoT devices is of considerable importance. In this context, we propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in a first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in a second phase, the UAV collects data from the IoT devices through information transmission. To characterise the agility of the UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at maximizing the total network sum-rate, we jointly optimize the trajectory of the UAV, the energy harvesting scheduling of IoT devices, and the phaseshift matrix of the RIS. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate. Numerical results illustrate the effectiveness of the UAV's flying path optimization and the network's throughput of our proposed techniques compared with other benchmark schemes. Given the strict requirements of the RIS and UAV, the significant improvement in processing time and throughput performance demonstrates that our proposed scheme is well applicable for practical IoT applications.