5 papers · 1 filter
Low-complexity Samples versus Symbols-based Neural Network Receiver for Channel Equalization
Yevhenii Osadchuk, Ognjen Jovanovic, Stenio M. Ranzini +4
Low-complexity neural networks (NNs) have successfully been applied for digital signal processing (DSP) in short-reach intensity-modulated directly detected optical links, where ch…
Reservoir Computing-based Multi-Symbol Equalization for PAM 4 Short-reach Transmission
Yevhenii Osadchuk, Ognjen Jovanovic, Darko Zibar +1
We propose spectrum-sliced reservoir computer-based (RC) multi-symbol equalization for 32-GBd PAM4 transmission. RC with 17 symbols at the output achieves an order of magnitude red…
Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications
Pedro J. Freire, Yevhenii Osadchuk, Antonio Napoli +5
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 sever…
Experimental Study of Deep Neural Network Equalizers Performance in Optical Links
Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler +5
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…
Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems
Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler +5
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 equalizatio…