Research Paper#Wireless Communication, Reinforcement Learning, UAV, RIS🔬 ResearchAnalyzed: Jan 3, 2026 08:42
Throughput Optimization in UAV-Mounted RIS using DRL
Published:Dec 31, 2025 10:36
•1 min read
•ArXiv
Analysis
This paper addresses a practical problem in wireless communication: optimizing throughput in a UAV-mounted Reconfigurable Intelligent Surface (RIS) system, considering real-world impairments like UAV jitter and imperfect channel state information (CSI). The use of Deep Reinforcement Learning (DRL) is a key innovation, offering a model-free approach to solve a complex, stochastic, and non-convex optimization problem. The paper's significance lies in its potential to improve the performance of UAV-RIS systems in challenging environments, while also demonstrating the efficiency of DRL-based solutions compared to traditional optimization methods.
Key Takeaways
- •Proposes a DRL-based solution for throughput optimization in UAV-mounted RIS systems.
- •Addresses practical impairments like UAV jitter and imperfect CSI.
- •Achieves higher throughput than conventional methods under severe jitter and low CSI quality.
- •Offers significantly faster inference times compared to traditional optimization methods.
Reference
“The proposed DRL controllers achieve online inference times of 0.6 ms per decision versus roughly 370-550 ms for AO-WMMSE solvers.”