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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Convolutional long short-term memory neural network equalizer for nonlinear Fourier transform-based optical
Optics Express
|April 6, 2021
Summary
Artificial neural network equalisers significantly improve optical transmission systems. Bidirectional long short-term memory networks offer substantial bit-error rate reduction, enabling higher data rates over long distances.
Area of Science:
- Optical communications
- Signal processing
- Artificial intelligence
Background:
- Optical transmission systems face performance degradation due to nonlinear effects.
- Traditional equalisation methods struggle with complex nonlinear spectral correlations.
- Artificial neural networks offer potential for advanced signal recovery.
Purpose of the Study:
- To evaluate artificial neural network equalisers for optical transmission systems.
- To propose and assess novel bidirectional long short-term memory (BLSTM) based equaliser designs.
- To compare BLSTM equaliser performance against fully connected neural networks.
Main Methods:
- Implementation of BLSTM gated recurrent neural network equalisers.
- Comparison with feed-forward fully connected layer equalisers.
- Integration of a 1D convolutional layer for data pre-processing.
Main Results:
- BLSTM equalisers achieved a 16x improvement in bit-error rate (BER) compared to non-equalised systems.
- A data rate of 170 Gbit/s was achieved over 1000 km using BLSTM equalisers.
- Adding a 1D convolutional layer further enhanced BER by 23x, staying below the HD-FEC threshold.
Conclusions:
- BLSTM neural network equalisers significantly enhance optical transmission performance.
- The proposed BLSTM equaliser design effectively handles nonlinear spectral component correlations.
- Advanced neural network architectures show promise for future high-speed optical communication systems.
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