Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach
As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degree...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/8/2765 |
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| author | Hamidreza Taghvaee Akshay Jain Xavier Timoneda Christos Liaskos Sergi Abadal Eduard Alarcón Albert Cabellos-Aparicio |
| author_facet | Hamidreza Taghvaee Akshay Jain Xavier Timoneda Christos Liaskos Sergi Abadal Eduard Alarcón Albert Cabellos-Aparicio |
| author_sort | Hamidreza Taghvaee |
| collection | DOAJ |
| description | As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment. |
| first_indexed | 2024-03-10T12:20:22Z |
| format | Article |
| id | doaj.art-826a9cab2f914134b04821b74a01f5a4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:20:22Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-826a9cab2f914134b04821b74a01f5a42023-11-21T15:33:43ZengMDPI AGSensors1424-82202021-04-01218276510.3390/s21082765Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based ApproachHamidreza Taghvaee0Akshay Jain1Xavier Timoneda2Christos Liaskos3Sergi Abadal4Eduard Alarcón5Albert Cabellos-Aparicio6NaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, SpainNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, SpainNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain Foundation for Research and Technology Hellas, 71110 Heraklion, GreeceNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, SpainNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, SpainNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, SpainAs the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment.https://www.mdpi.com/1424-8220/21/8/2765metasurfacemachine learningneural networksbeam steeringradiation pattern5G and beyond |
| spellingShingle | Hamidreza Taghvaee Akshay Jain Xavier Timoneda Christos Liaskos Sergi Abadal Eduard Alarcón Albert Cabellos-Aparicio Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach Sensors metasurface machine learning neural networks beam steering radiation pattern 5G and beyond |
| title | Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach |
| title_full | Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach |
| title_fullStr | Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach |
| title_full_unstemmed | Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach |
| title_short | Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach |
| title_sort | radiation pattern prediction for metasurfaces a neural network based approach |
| topic | metasurface machine learning neural networks beam steering radiation pattern 5G and beyond |
| url | https://www.mdpi.com/1424-8220/21/8/2765 |
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