Federated learning via over-the-air computation in IRS-assisted UAV communications
Abstract Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are two key technologies in the sixth generation of mobile communication (6G). In this paper, IRS is equipped on UAV to form aerial IRS, which can achieve 360° panoramic full-angle reflection and flexible d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34292-8 |
| _version_ | 1797822909226418176 |
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| author | Ruijie Li Li Zhu Guoping Zhang Hongbo Xu Yun Chen |
| author_facet | Ruijie Li Li Zhu Guoping Zhang Hongbo Xu Yun Chen |
| author_sort | Ruijie Li |
| collection | DOAJ |
| description | Abstract Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are two key technologies in the sixth generation of mobile communication (6G). In this paper, IRS is equipped on UAV to form aerial IRS, which can achieve 360° panoramic full-angle reflection and flexible deployment of IRS. In order to achieve high-quality and ubiquitous network coverage under data privacy and low latency requirements, we propose an Federated learning (FL) network via Over-the-Air computation (AirComp) in IRS-assisted UAV communications. Our goal is to minimize the worst-case mean square error (MSE) by jointly optimizing the IRS phase shift, denoising factor for noise suppression, the user’s transmission power, and UAV trajectory. Optimizing and quickly adjusting the UAV position and IRS phase shift, it flexibly assists the signal transmission between users and base stations (BS). In order to solve this complex non-convex problem, we propose a low-complexity iterative algorithm, which divides the original problem into four sub-problems, respectively using the semi-definite programming (SDP) method, slack variable introduction method, successive convex approximation (SCA) method to solve each sub-problem. Through the analysis of simulation results, our proposed design scheme is obviously better than other benchmark schemes. |
| first_indexed | 2024-03-13T10:16:11Z |
| format | Article |
| id | doaj.art-d882baf2b5e041cda42ceff623c9415f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T10:16:11Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d882baf2b5e041cda42ceff623c9415f2023-05-21T11:15:36ZengNature PortfolioScientific Reports2045-23222023-05-0113111110.1038/s41598-023-34292-8Federated learning via over-the-air computation in IRS-assisted UAV communicationsRuijie Li0Li Zhu1Guoping Zhang2Hongbo Xu3Yun Chen4College of Physical Science and Technology, Central China Normal UniversityCollege of Physical Science and Technology, Central China Normal UniversityCollege of Physical Science and Technology, Central China Normal UniversityCollege of Physical Science and Technology, Central China Normal UniversityCollege of Physical Science and Technology, Central China Normal UniversityAbstract Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are two key technologies in the sixth generation of mobile communication (6G). In this paper, IRS is equipped on UAV to form aerial IRS, which can achieve 360° panoramic full-angle reflection and flexible deployment of IRS. In order to achieve high-quality and ubiquitous network coverage under data privacy and low latency requirements, we propose an Federated learning (FL) network via Over-the-Air computation (AirComp) in IRS-assisted UAV communications. Our goal is to minimize the worst-case mean square error (MSE) by jointly optimizing the IRS phase shift, denoising factor for noise suppression, the user’s transmission power, and UAV trajectory. Optimizing and quickly adjusting the UAV position and IRS phase shift, it flexibly assists the signal transmission between users and base stations (BS). In order to solve this complex non-convex problem, we propose a low-complexity iterative algorithm, which divides the original problem into four sub-problems, respectively using the semi-definite programming (SDP) method, slack variable introduction method, successive convex approximation (SCA) method to solve each sub-problem. Through the analysis of simulation results, our proposed design scheme is obviously better than other benchmark schemes.https://doi.org/10.1038/s41598-023-34292-8 |
| spellingShingle | Ruijie Li Li Zhu Guoping Zhang Hongbo Xu Yun Chen Federated learning via over-the-air computation in IRS-assisted UAV communications Scientific Reports |
| title | Federated learning via over-the-air computation in IRS-assisted UAV communications |
| title_full | Federated learning via over-the-air computation in IRS-assisted UAV communications |
| title_fullStr | Federated learning via over-the-air computation in IRS-assisted UAV communications |
| title_full_unstemmed | Federated learning via over-the-air computation in IRS-assisted UAV communications |
| title_short | Federated learning via over-the-air computation in IRS-assisted UAV communications |
| title_sort | federated learning via over the air computation in irs assisted uav communications |
| url | https://doi.org/10.1038/s41598-023-34292-8 |
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