A 1-bit reconfigurable intelligent metasurface-based antenna design for 5G application

Zhou, Yajun; Chen, Lianfeng; Zhang, Qifei; Wang, Hao; Guo, Linyan

doi:10.1017/s1759078723000466

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…RIS improves the coverage and quality of signal by intelligently controlling the propagation environment. RIS uses low-cost reflecting metasurface (MTS) integrated with switching components like PIN diodes [6][7][8][9][10][11][12][13][14][15][16][17], varactor diodes [18,19], and a smart controllers. MTS exhibits special properties due to its ability to modify the incident electromagnetic (EM) waves on its surface.…”

Section: Introductionmentioning

confidence: 99%

A New Unit-Cell Design for a 2-Bit Reflective Metasurface for RIS Applications

Fazal,

Hong

2023

Electronics

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In this research, a novel unit-cell design for a 2-bit reflective metasurface using the theory of characteristic modes is presented. The initial unit-cell consists of three sub-patches with 2-PIN diodes integrated within the gaps between sub-patches. The initial structure provides limited tuning of the phase curves with the change in switching states of PIN diodes. To provide the phase deviation required for the 2-bit operation, the symmetry of the modal currents is used to propose multiple slots in the sub-patches. Slots are introduced to lower the resonant frequencies, as well as provide tuning of the resonance of modes and consequently phase change between different switching states. For further resonance tuning and phase optimization, two vias are loaded in the middle patch to provide a 90° phase difference between each switching state. Simulated results show that the proposed unit-cell design with the aid of characteristic modes has a smaller size and provides 2-bit operation in the frequency range of 3.9–4.04 GHz.

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Section: Introductionmentioning

confidence: 99%

A New Unit-Cell Design for a 2-Bit Reflective Metasurface for RIS Applications

Fazal,

Hong

2023

Electronics

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Robust adaptive beamforming via residual convolutional neural network

Liu,

Qin,

et al. 2023

Int. J. Microw. Wireless Technol.

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Aiming at the problem that the covariance matrix includes the desired signal and the signal steer vector mismatches will degrade the beamforming performance, an effective robust adaptive beamforming (RAB) approach is presented in this paper based on a residual convolutional neural network (RAB-RCNN). In the presented method, the RAB-RCNN model is designed by introducing a residual unit, which can extract the deeper features from the signal sample covariance matrix. Residual noise elimination and interferences power estimation are utilized to reconstruct the desired signal covariance matrix, and correct the mismatched steering vector (SV) by the eigenvalue decomposition of the reconstructed desired signal covariance matrix. The projection method is utilized to redesign the signal interference-plus-noise covariance matrix. Furthermore, the beamforming weight vector is calculated with the two parameters obtained before and used as the label of the RAB-RCNN model, The trained model can rapidly and precisely output the predicted beamforming weight vector without complex matrix operations, including the matrix inversion of the signal covariance matrix, so that the calculation time can be reduced for beamforming. Simulations demonstrate the robustness of the presented approach against SV mismatches due to the direction-of-arrival estimation error, sensor position error, and local scattering interference.

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A Frequency Sensor of Reconfigurable Intelligent Metasurface for 5G Communication

Cui,

Guo

2024

2024 Photonics &Amp; Electromagnetics Research Symposium (PIERS)

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A 1-bit reconfigurable intelligent metasurface-based antenna design for 5G application

Cited by 4 publications

References 35 publications

A New Unit-Cell Design for a 2-Bit Reflective Metasurface for RIS Applications

A New Unit-Cell Design for a 2-Bit Reflective Metasurface for RIS Applications

Robust adaptive beamforming via residual convolutional neural network

A Frequency Sensor of Reconfigurable Intelligent Metasurface for 5G Communication

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