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Published on: November 26, 2019
UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning
Junjie Yan1,2, Yichen Xu1,2, Haohao Yuan1,2
1School of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China.
This study introduces a new method for Unmanned Aerial Vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to improve communication. It optimizes UAV angles and RIS settings to boost service for all users, reducing overall system time.
Area of Science:
- Wireless Communication
- Aerial Networks
- Metamaterials
Background:
- Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs) are key for enhancing wireless communication coverage.
- Current research often overlooks the impact of UAV hovering angles on signal reflection and transmission with STAR-RIS.
Purpose of the Study:
- To propose a novel STAR-RIS-assisted UAV mechanism for dynamic service area partitioning based on real-time user distribution.
- To enhance channel quality for edge and occluded users by jointly optimizing UAV trajectory, hovering angle, and STAR-RIS parameters.
Main Methods:
- Decomposition of the complex optimization problem into subproblems.
- Chained Lin-Kernighan (CLK) algorithm for UAV flight trajectory optimization.
- TD3 algorithm for optimizing STAR-RIS parameters and UAV hovering angle.
Main Results:
- Significant improvement in channel quality for edge and occluded users.
- Effective reduction in system service time and user transmission time.
- Demonstrated superiority over traditional methods in performance metrics.
Conclusions:
- The proposed mechanism successfully addresses co-channel interference in dynamically partitioned service areas.
- Joint optimization of UAV trajectory, hovering angle, and STAR-RIS parameters is crucial for advanced wireless systems.
- This approach offers a significant advancement in UAV-RIS integrated communication systems.
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