UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning
UAVs and reconfigurable intelligent surfaces (RISs) have emerged as promising solutions to enhance communication coverage and performance. However, existing studies primarily focus on optimizing the amplitude and phase shift of a STAR-RIS without considering the impact of varying UAV hovering angles...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/6/1943 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850088259858726912 |
|---|---|
| author | Junjie Yan Yichen Xu Haohao Yuan Chunhua Xue |
| author_facet | Junjie Yan Yichen Xu Haohao Yuan Chunhua Xue |
| author_sort | Junjie Yan |
| collection | DOAJ |
| description | UAVs and reconfigurable intelligent surfaces (RISs) have emerged as promising solutions to enhance communication coverage and performance. However, existing studies primarily focus on optimizing the amplitude and phase shift of a STAR-RIS without considering the impact of varying UAV hovering angles on signal reflection and transmission. In this paper, we propose a novel STAR-RIS-assisted UAV service enhancement mechanism that dynamically adjusts reflection/transmission regions based on the real-time user distribution, significantly improving the channel quality for both edge and occluded users. This work is the first to jointly optimize the phase and amplitude of the STAR-RIS, the UAV flight trajectory, and the hovering angle, addressing the critical challenge of co-channel interference caused by dynamically partitioned service areas. The complex optimization problem is decomposed into subproblems, where the UAV flight trajectory is optimized using the Chained Lin–Kernighan (CLK) algorithm and the STAR-RIS parameters and UAV hovering angle are optimized using the TD3 algorithm. The experimental results show that the proposed mechanism effectively reduces the system service time and user transmission time, outperforming traditional methods. |
| format | Article |
| id | doaj-art-715fde1583ed447c8bd5363f6c8f6fb4 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-715fde1583ed447c8bd5363f6c8f6fb42025-08-20T02:43:03ZengMDPI AGSensors1424-82202025-03-01256194310.3390/s25061943UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement LearningJunjie Yan0Yichen Xu1Haohao Yuan2Chunhua Xue3School of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaUAVs and reconfigurable intelligent surfaces (RISs) have emerged as promising solutions to enhance communication coverage and performance. However, existing studies primarily focus on optimizing the amplitude and phase shift of a STAR-RIS without considering the impact of varying UAV hovering angles on signal reflection and transmission. In this paper, we propose a novel STAR-RIS-assisted UAV service enhancement mechanism that dynamically adjusts reflection/transmission regions based on the real-time user distribution, significantly improving the channel quality for both edge and occluded users. This work is the first to jointly optimize the phase and amplitude of the STAR-RIS, the UAV flight trajectory, and the hovering angle, addressing the critical challenge of co-channel interference caused by dynamically partitioned service areas. The complex optimization problem is decomposed into subproblems, where the UAV flight trajectory is optimized using the Chained Lin–Kernighan (CLK) algorithm and the STAR-RIS parameters and UAV hovering angle are optimized using the TD3 algorithm. The experimental results show that the proposed mechanism effectively reduces the system service time and user transmission time, outperforming traditional methods.https://www.mdpi.com/1424-8220/25/6/1943STAR-RISUAV-enhanced edge servicesresource allocationdeep reinforcement learning |
| spellingShingle | Junjie Yan Yichen Xu Haohao Yuan Chunhua Xue UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning Sensors STAR-RIS UAV-enhanced edge services resource allocation deep reinforcement learning |
| title | UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning |
| title_full | UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning |
| title_fullStr | UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning |
| title_full_unstemmed | UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning |
| title_short | UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning |
| title_sort | uav onboard star ris service enhancement mechanism based on deep reinforcement learning |
| topic | STAR-RIS UAV-enhanced edge services resource allocation deep reinforcement learning |
| url | https://www.mdpi.com/1424-8220/25/6/1943 |
| work_keys_str_mv | AT junjieyan uavonboardstarrisserviceenhancementmechanismbasedondeepreinforcementlearning AT yichenxu uavonboardstarrisserviceenhancementmechanismbasedondeepreinforcementlearning AT haohaoyuan uavonboardstarrisserviceenhancementmechanismbasedondeepreinforcementlearning AT chunhuaxue uavonboardstarrisserviceenhancementmechanismbasedondeepreinforcementlearning |