Mitigation of nonlinear effects using machine learning in coherent optical access networks
Introduction— The use of coherent detection jointly with high-level modulation formats such as 16 and 64-QAM seems to be a convenient strategy to increment capacity of future optical access networks. However, coherent detection requires high complexity digital signal processing to mitigate different...
- Autores:
-
Escobar, Alejandro
Arroyave, Karen
LOPERA CORTES, JHON ANDERSON
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
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10224
- Acceso en línea:
- https://hdl.handle.net/11323/10224
https://repositorio.cuc.edu.co/
- Palabra clave:
- Coherent communications
Digital signal processing
Machine learning
Optical access networks
Quadrature amplitude modulation
Aprendizaje de Máquina
Comunicaciones coherentes
Modulación de amplitud en cuadratura
Procesamiento digital de señales
Redes ópticas de acceso
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
| Summary: | Introduction— The use of coherent detection jointly with high-level modulation formats such as 16 and 64-QAM seems to be a convenient strategy to increment capacity of future optical access networks. However, coherent detection requires high complexity digital signal processing to mitigate different impairments. Objective— Mitigate signal distortions using nonsymmetrical demodulation techniques based on Machine Learning (ML) algorithms. Methodology— First, a single channel Nyquist m-QAM system at 28 and 32 Gbps was simulated in VPIDesignSuite software. Then, different signals modulated at 16 and 64-QAM were generated with different laser linewidth, transmission distances and launch powers. Two ML algorithms were implemented to carry out the demodulation of the generated signals. The performance of the algorithms was evaluated using the Bit Error Rate (BER) in terms of different system parameters as laser linewidth, transmission distance, launch power and modulation format. Results— The use of ML allowed gains up to 2 dB in terms of optical signal-to-noise ratio at a BER value of for 16-QAM and 1.5 dB for 64-QAM. Also, the use of ML showed that it is possible to use a lower cost laser (100 kHz linewidth vs 25 kHz) with a better BER performance than using conventional demodulation. Conclusions— We showed that the use of both algorithms could mitigate nonlinear effects and could reduce computational complexity for future optical access networks. |
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