volume | PIER Journals

Abstract

Orbital angular momentum (OAM) becomes a new resource for wireless communication due to the different modes being orthogonal. In OAM-based wireless communications, factors such as tilt and multipath distort the phase of the OAM beam, making the mode difficult to detect. We propose a multi-label class-specific lightweight neural network (MCSLNN) to measure tilt and detect mode from a single image. MCSLNN utilizes the MobileNetV2 network as the backbone feature extraction network, considering the terminal devices with limited computing resources. To improve the performance of multi-label classification, MCSLNN employs residual class-specific attention (CSRA) as the classification layer. Furthermore, MCSLNN employs the beam steering method to verify the correctness of the measured tilt. The network measures the tilt angle with an accuracy of 76% and an estimation error of ±1° in a validation experiment. Finally, we analyze the network's generalization from varying heights above the ground for reflection paths. The results indicate that MCSLNN is adaptable to diverse circumstances, thus making it suitable for 6G communication and radar applications.

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Mr. Hui Yang

Ms. Yifei Cheng

Dr. Zhong Yu

Mr. Zhe Wang

Dr. Yi Lu