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Found 6 papers, 0 papers with code
Low-complexity Samples versus Symbols-based Neural Network Receiver for Channel Equalization
In this work, we propose a low-complexity NN that performs samples-to-symbol equalization, meaning that the NN-based equalizer includes match filtering and downsampling.
Reservoir Computing-based Multi-Symbol Equalization for PAM 4 Short-reach Transmission
We propose spectrum-sliced reservoir computer-based (RC) multi-symbol equalization for 32-GBd PAM4 transmission.
Deep Neural Network-aided Soft-Demapping in Optical Coherent Systems: Regression versus Classification
We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in coherent optical communications, where the transmission channel is nonlinear and dispersive.
Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications
Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of several neural network architectures, presenting the results for TWC and SSMF set-ups.
Experimental Study of Deep Neural Network Equalizers Performance in Optical Links
We propose a convolutional-recurrent channel equalizer and experimentally demonstrate 1dB Q-factor improvement both in single-channel and 96 x WDM, DP-16QAM transmission over 450km of TWC fiber.
Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems
We present the results of the comparative analysis of the performance versus complexity for several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems.
