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Compensation of nonlinear distortion in coherent optical OFDM systems using a MIMO deep neural network-based
Optics Letters
|October 15, 2020
Summary
A novel multiple-input multiple-output deep neural network (MIMO-DNN) effectively compensates for optical nonlinearities. This advanced digital signal processing extends transmission reach by 4 dB, outperforming existing methods.
Area of Science:
- Optical communications
- Digital signal processing
- Machine learning
Background:
- Inter-subcarrier nonlinearities degrade performance in high-speed optical systems.
- Accurate compensation is crucial for extending transmission distance and maintaining signal integrity.
Purpose of the Study:
- To propose and demonstrate a novel multiple-input multiple-output deep neural network (MIMO-DNN) equalizer.
- To compensate for inter-subcarrier nonlinearities in a 40 Gb/s coherent optical orthogonal frequency division multiplexing (CO-OFDM) system.
Main Methods:
- Development of a MIMO-DNN based nonlinear equalizer.
- Experimental demonstration in a 40 Gb/s CO-OFDM system with 2000 km standard single-mode fiber transmission.
Main Results:
- MIMO-DNN extends the power margin by 4 dB compared to linear compensation and single-input single-output DNN.
- MIMO-DNN improves the effective Q-factor by up to 1 dB over digital back propagation.
- MIMO-DNN reduces computational cost by a factor of three compared to digital back propagation.
Conclusions:
- MIMO-DNN is a highly effective method for compensating inter-subcarrier nonlinearities in CO-OFDM systems.
- The proposed MIMO-DNN offers significant advantages in terms of transmission reach, signal quality, and computational efficiency.
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