Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
Erick Lamilla,
Christian Sacarelo,
Manuel S. Alvarez-Alvarado,
Arturo Pazmino,
Peter Iza
Affiliations
Erick Lamilla
Escuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, Ecuador
Christian Sacarelo
Escuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, Ecuador
Manuel S. Alvarez-Alvarado
Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación(FIEC), Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, Ecuador
Arturo Pazmino
Escuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, Ecuador
Peter Iza
Escuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, Ecuador
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.
