Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

Taghvaee, Hamidreza; Jain, Akshay; Timoneda, Xavier; Liaskos, Christos; Abadal, Sergi; Alarcón, Eduard; Cabellos-Aparicio, Albert

Abstract

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.

Citation

Taghvaee, H., Jain, A., Timoneda, X., Liaskos, C., Abadal, S., Alarcón, E., & Cabellos-Aparicio, A. (2021). Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach. Sensors, 21(8), Article 2765. https://doi.org/10.3390/s21082765

• Journal Article Type: Article
• Acceptance Date: Apr 2, 2021
• Online Publication Date: Apr 14, 2021
• Publication Date: Apr 2, 2021
• Deposit Date: Jan 24, 2023
• Publicly Available Date: Feb 2, 2023
• Journal: Sensors
• Print ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 8
• Article Number: 2765
• DOI: https://doi.org/10.3390/s21082765
• Keywords: Electrical and Electronic Engineering; Biochemistry; Instrumentation; Atomic and Molecular Physics, and Optics; Analytical Chemistry
• Public URL: https://nottingham-repository.worktribe.com/output/13465535
• Publisher URL: https://www.mdpi.com/1424-8220/21/8/2765