Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learning
Abstract This paper introduces the SAC-IRS algorithm, a novel approach that combines Intelligent Reflecting Surfaces (IRS) and Reinforcement Learning (RL) to optimize network performance, specifically targeting packet delivery rate and energy efficiency. The proposed algorithm dynamically adjusts IR...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-07552-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849343392796901376 |
|---|---|
| author | Chengfu Fan Shoujiu Xiong Fei Zhou |
| author_facet | Chengfu Fan Shoujiu Xiong Fei Zhou |
| author_sort | Chengfu Fan |
| collection | DOAJ |
| description | Abstract This paper introduces the SAC-IRS algorithm, a novel approach that combines Intelligent Reflecting Surfaces (IRS) and Reinforcement Learning (RL) to optimize network performance, specifically targeting packet delivery rate and energy efficiency. The proposed algorithm dynamically adjusts IRS phase shifts at the physical layer, leveraging cross-layer optimization to coordinate the physical, link, and network layers. Unlike traditional approaches, SAC-IRS incorporates real-time channel state information (CSI) feedback, enabling the system to adapt to varying network conditions and improve signal strength while reducing energy consumption. Experimental results demonstrate that SAC-IRS achieves a 90% packet delivery rate and lowers energy consumption to 0.25 mJ/bit, outperforming existing methods such as AODV, DeepIR, and IRS-Q-Routing. These findings highlight the algorithm’s ability to balance signal optimization with energy efficiency. Additionally, the paper discusses the challenges of deploying IRS hardware in real-world settings, addressing issues such as scalability in large networks, handling node mobility, and adapting to dynamic topology changes. The proposed protocol’s real-world applicability is considered, and future directions are outlined for overcoming scalability and adaptation challenges in practical deployments. |
| format | Article |
| id | doaj-art-503ba2c0851a4c0c8a6ced42598da065 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-503ba2c0851a4c0c8a6ced42598da0652025-08-20T03:43:01ZengSpringerDiscover Applied Sciences3004-92612025-07-017814610.1007/s42452-025-07552-7Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learningChengfu Fan0Shoujiu Xiong1Fei Zhou2School of Computer Engineering, Anhui Wenda University of Information EngineeringSchool of Computer Engineering, Anhui Wenda University of Information EngineeringSchool of Computer Engineering, Anhui Wenda University of Information EngineeringAbstract This paper introduces the SAC-IRS algorithm, a novel approach that combines Intelligent Reflecting Surfaces (IRS) and Reinforcement Learning (RL) to optimize network performance, specifically targeting packet delivery rate and energy efficiency. The proposed algorithm dynamically adjusts IRS phase shifts at the physical layer, leveraging cross-layer optimization to coordinate the physical, link, and network layers. Unlike traditional approaches, SAC-IRS incorporates real-time channel state information (CSI) feedback, enabling the system to adapt to varying network conditions and improve signal strength while reducing energy consumption. Experimental results demonstrate that SAC-IRS achieves a 90% packet delivery rate and lowers energy consumption to 0.25 mJ/bit, outperforming existing methods such as AODV, DeepIR, and IRS-Q-Routing. These findings highlight the algorithm’s ability to balance signal optimization with energy efficiency. Additionally, the paper discusses the challenges of deploying IRS hardware in real-world settings, addressing issues such as scalability in large networks, handling node mobility, and adapting to dynamic topology changes. The proposed protocol’s real-world applicability is considered, and future directions are outlined for overcoming scalability and adaptation challenges in practical deployments.https://doi.org/10.1007/s42452-025-07552-7Spatiotemporal reinforcement learningIntelligent reflecting surfaceLow-power routing protocolHeterogeneous network topologyCross-layer optimization |
| spellingShingle | Chengfu Fan Shoujiu Xiong Fei Zhou Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learning Discover Applied Sciences Spatiotemporal reinforcement learning Intelligent reflecting surface Low-power routing protocol Heterogeneous network topology Cross-layer optimization |
| title | Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learning |
| title_full | Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learning |
| title_fullStr | Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learning |
| title_full_unstemmed | Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learning |
| title_short | Optimizing low-power, anti-interference routing with IRS through spatiotemporal reinforcement and federated learning |
| title_sort | optimizing low power anti interference routing with irs through spatiotemporal reinforcement and federated learning |
| topic | Spatiotemporal reinforcement learning Intelligent reflecting surface Low-power routing protocol Heterogeneous network topology Cross-layer optimization |
| url | https://doi.org/10.1007/s42452-025-07552-7 |
| work_keys_str_mv | AT chengfufan optimizinglowpowerantiinterferenceroutingwithirsthroughspatiotemporalreinforcementandfederatedlearning AT shoujiuxiong optimizinglowpowerantiinterferenceroutingwithirsthroughspatiotemporalreinforcementandfederatedlearning AT feizhou optimizinglowpowerantiinterferenceroutingwithirsthroughspatiotemporalreinforcementandfederatedlearning |