Direct Decoding Of Nonlinear Ofdm-Qam Signals Using Convolutional Neural Network

Nonlinear Fourier transform, as a technique that has a great potential to overcome the capacity limit in fibre optical communication system, faces speed and accuracy bottlenecks in practice. Machine learning using convolutional neural networks shows great potential in NFT-based applications. We have developed a convolutional neural network for decoding information in NFT-based communication and numerically demonstrated its performance in comparison to a fast NFT algorithm. The comparison indicates the potential of conventional neural network to replace NFT calculations for decoding of information. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement