Abstract
We evaluate improvement in the performance of the optical transmission systems operating with the continuous nonlinear Fourier spectrum by the artificial neural network equalisers installed at the receiver end. We propose here a novel equaliser designs based on bidirectional long short-term memory (BLSTM) gated recurrent neural network and compare their performance with the equaliser based on several fully connected layers. The proposed approach accounts for the correlations between different nonlinear spectral components. The application of BLSTM equaliser leads to a 16x improvement in terms of bit-error rate (BER) compared to the non-equalised case. The proposed equaliser makes it possible to reach the data rate of 170 Gbit/s for one polarisation conventional nonlinear Fourier transform (NFT) based system at 1000 km distance. We show that our new BLSTM equalisers significantly outperform the previously proposed scheme based on a feed-forward fully connected neural network. Moreover, we demonstrate that by adding a 1D convolutional layer for the data pre-processing before BLSTM recurrent layers, we can further enhance the performance of the BLSTM equaliser, reaching 23x BER improvement for the 170 Gbit/s system over 1000 km, staying below the 7% forward error correction hard decision threshold (HD-FEC).
Highlights
Increasing demand for capacity of optical communication systems incites active studies to reach the data rates higher than those provided by the current-generation systems [1,2,3]
This work investigates the efficiency of such equalisers utilising artificial neural networks (NN) of two types: the feed-forward and bidirectional long short-term memory (BLSTM) gated recurrent NN approaches which are used to deal with the noise and nonlinear Fourier transform (NFT) processing impairments affecting the quality of data transition inside the nonlinear Fourier (NF) domain
In this paper we addressed the question of how we can improve the performance of the NFT-based optical transmission systems and make them more robust in the practical environment
Summary
Increasing demand for capacity of optical communication systems incites active studies to reach the data rates higher than those provided by the current-generation systems [1,2,3]. This work investigates the efficiency of such equalisers utilising artificial neural networks (NN) of two types: the feed-forward and bidirectional long short-term memory (BLSTM) gated recurrent NN approaches which are used to deal with the noise and NFT processing impairments affecting the quality of data transition inside the NF domain. The classification approach is failing in the case when we have the intersection of clouds in the received constellation [24] For this case, we have already shown that, in order to attain the higher performance gain, we can employ the FFNN-based equaliser directly to the received nonlinear spectrum (NS) after dispersion compensation: such a technique renders much better equalisation results [24]. We anticipate that this performance improving method can be applicable for the equalisation of a wide range of systems dealing with continuous NS modulation
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