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Two-stage artificial neural network-based burst-subcarrier joint equalization in nonlinear frequency division
Optics Letters
|April 1, 2021
Summary
We introduce an artificial neural network (ANN) scheme to enhance nonlinear frequency division multiplexing (NFDM) optical systems. This method jointly mitigates time and frequency distortions, significantly improving signal quality over long fiber distances.
Area of Science:
- Optical communications
- Artificial intelligence in telecommunications
- Signal processing
Background:
- Nonlinear frequency division multiplexing (NFDM) systems face performance degradation due to time-domain distortions and frequency-domain crosstalk.
- Existing equalization techniques often address these impairments separately, limiting overall system efficiency.
Purpose of the Study:
- To propose and validate an artificial neural network (ANN)-based scheme for joint time and frequency domain equalization in NFDM optical transmission systems.
- To improve the performance and robustness of NFDM systems against complex signal impairments.
Main Methods:
- A two-stage artificial neural network (ANN) equalizer architecture is proposed, comprising a Burst ANN and a Subcarrier ANN.
- The scheme is implemented at the receiver side to jointly mitigate inter-burst distortions and inter-subcarrier crosstalk.
- Numerical simulations are conducted on a dual-polarization NFDM transmission system.
Main Results:
- The proposed two-stage ANN equalizer achieves a Q-factor gain of 3.01 dB compared to a basic detection scheme.
- Significant joint mitigation of time-domain distortions and frequency-domain crosstalk is demonstrated.
- The system operates at a gross data rate of 256 Gb/s over 960 km of standard single-mode fiber (SSMF).
Conclusions:
- The two-stage ANN equalization approach provides an effective solution for jointly addressing multidimensional signal impairments in NFDM systems.
- This method offers a substantial performance improvement, paving the way for more advanced optical transmission technologies.
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