Mitigation of Nonlinear Effects using Machine Learning in Coherent Optical Access Networks

Introduction/Context: 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 1x10(-2) 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.