Multi-beam radio frequency oam positioning identification method and system based on deep learning

By using a deep learning-based multi-beam radio frequency OAM positioning and recognition method, which utilizes convolutional neural networks to automatically identify radio frequency orbital angular momentum patterns, the problem of low efficiency in manual recognition in existing technologies is solved, and efficient and accurate automated recognition is achieved.

CN117119382BActive Publication Date: 2026-06-19NANJING UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING UNIV OF SCI & TECH
Filing Date
2023-08-21
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing technologies require manual determination of position and number of modes when identifying radio frequency orbital angular momentum images, resulting in low recognition efficiency.

⚗Method used

A deep learning-based multi-beam radio frequency OAM positioning and recognition method is adopted. The convolutional neural network is used to pre-train the optical orbital angular momentum image, and the deep learning model is used to automatically identify the orbital angular momentum pattern and position. The radio frequency orbital angular momentum image dataset generated by simulated UCA is used for training and preprocessing to improve the recognition accuracy and efficiency.

🎯Benefits of technology

It achieves automated and information-based orbital angular momentum pattern recognition, improves recognition efficiency, enhances robustness against noise interference, and ensures accuracy and speed of recognition.

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Abstract

The application discloses a kind of based on deep learning multi-beam radio frequency OAM positioning identification method and system, the method and system use optical orbital angular momentum as the basis of pre-training, using pre-training weight information trains radio frequency orbital angular momentum image, and realizes the positioning and identification function of radio frequency orbital angular momentum.The trained deep learning model of the application can automatically identify the real radio frequency orbital angular momentum image received in real time, without manual discrimination, thereby simplifying the operation process of technical personnel.The application reduces the training time, realizes the automation of identification, improves the spectrum utilization, and helps the demultiplexing system of radio frequency OAM system.
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