Lamilla, E.; Sacarelo, C.; Alvarez-Alvarado, M.S.; Pazmino, A.; Iza, P. Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning. Sensors2023, 23, 2755.
Lamilla, E.; Sacarelo, C.; Alvarez-Alvarado, M.S.; Pazmino, A.; Iza, P. Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning. Sensors 2023, 23, 2755.
Lamilla, E.; Sacarelo, C.; Alvarez-Alvarado, M.S.; Pazmino, A.; Iza, P. Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning. Sensors2023, 23, 2755.
Lamilla, E.; Sacarelo, C.; Alvarez-Alvarado, M.S.; Pazmino, A.; Iza, P. Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning. Sensors 2023, 23, 2755.
Abstract
Based on orbital angular momentum (OAM) properties of Laguerre Gaussian beams LG(p,ℓ), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and Machine-learning detection method. In the encoding process, the intensity profile for data encoding is generated based on selection of p and ℓ indices, while the decoding process is performed using support vector machine (SVM) algorithm. Two different decoding models based on SVM algorithm are tested to verify the robustness of optical encoding model, finding a BER =10−9 for 10.2dB of signal-to-noise ratio in one of the SVM models.
Keywords
Machine Learning; LG-beams; OAM-beams; Optical Encoding Model
Subject
Physical Sciences, Optics and Photonics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
