Mitigation of distortions in radio-over-fiber systems using machine learning

Introduction— The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireles...

Full description

Autores:: Torres Vahos, Diego Fernando
Escobar, Alejandro
Díaz Rodriguez, Cristian Alexis
Granada Torres, Jhon James

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

Description
Summary:	Introduction— The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireless transmissions, avoiding the bottleneck of optical-to-electrical conversion interfaces. However, the chromatic dispersion of the optical fiber generates distortions in the radiofrequency signals optically modulated, limiting the reach of transmission. Objective— To improve the performance of a Radioover-Fiber system in terms of bit-error-rate, using nonsymmetrical demodulation by means of the machine learning algorithm Support Vector Machine. Methodology— A Radio-over-Fiber System is simulated in the specialized software VPIDesignSuite. The radiofrequency signals are modulated at 16 and 64-QAM formats with different laser linewidths and transmitted over optical fiber. The Support Vector Machine algorithm is applied to carry out nonsymmetrical demodulation. Results— The implementation of the machine learning algorithm for signal demodulation significantly improves the network performance, reaching transmissions up to 30 km. It implies a reduction of the bit-error-rate up to two orders of magnitude in comparison with conventional demodulation. Conclusions— Mitigation of distortions in terms of bit-error-rate is demonstrated in a Radio-over-Fiber system using nonsymmetrical demodulation by using the Support Vector Machine algorithm. Thus, the proposed technique can be suitable for future high-capacity access networks.

Mitigation of distortions in radio-over-fiber systems using machine learning

Publicaciones similares