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. 2023 Mar 2;23(5):2755.
doi: 10.3390/s23052755.

Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning

Erick Lamilla  1   2 , Christian Sacarelo  1 , Manuel S Alvarez-Alvarado  3 , Arturo Pazmino  1 , Peter Iza  1   4
Affiliations

Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning

Erick Lamilla et al. Sensors (Basel). .

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 an intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and a machine learning detection method. In the encoding process, the intensity profile for data encoding is generated based on the selection of p and ℓ indices, while the decoding process is performed using a support vector machine (SVM) algorithm. Two different decoding models based on an SVM algorithm are tested to verify the robustness of the optical encoding model, finding a BER =10-9 for 10.2 dB of signal-to-noise ratio in one of the SVM models.

Keywords: LG-beams; OAM-beams; machine learning; optical encoding model.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
(a) Flow diagram of an SVM algorithm. The ensemble classifiers consist of a set of weak classifiers. The weights (wn) of the incorrectly predicted points are increased in the next classifier. The final decision is based on the weighted average of the individual predictions; (b) flowchart of the application of the support vector machine (SVM) algorithm in the decoding processing.
Figure 1
Figure 1
Concept and proposed setup of an optical encoding model. LS: laser source; PBS: polarization beam splitter, M1,2: mirror; BS1,2: beam splitter; PD: photodetector.
Figure 2
Figure 2
Data symbol set based on a 4-bit data symbol for the case study presented.
Figure 3
Figure 3
Different data symbols and their corresponding normalized intensity curves: (a.i) data symbol 0011; (b.i) data symbol 0110; (c.i) data symbol 1011; (a.ii,b.ii,c.ii) linear transformation of (a.i,b.i,c.i); (a.iii,b.iii,c.iii) normalized intensity curve corresponding to a pixel array of a 2D image (dotted yellow line) with different channel noise levels.
Figure 4
Figure 4
Computed BER for each SVM-ECOC model as a function of SNR for critical noise levels (from 0 to 14 dB).
See this image and copyright information in PMC

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