15 research outputs found
Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders
We investigate the potential of adaptive blind equalizers based on
variational inference for carrier recovery in optical communications. These
equalizers are based on a low-complexity approximation of maximum likelihood
channel estimation. We generalize the concept of variational autoencoder (VAE)
equalizers to higher order modulation formats encompassing probabilistic
constellation shaping (PCS), ubiquitous in optical communications, oversampling
at the receiver, and dual-polarization transmission. Besides black-box
equalizers based on convolutional neural networks, we propose a model-based
equalizer based on a linear butterfly filter and train the filter coefficients
using the variational inference paradigm. As a byproduct, the VAE also provides
a reliable channel estimation. We analyze the VAE in terms of performance and
flexibility over a classical additive white Gaussian noise (AWGN) channel with
inter-symbol interference (ISI) and over a dispersive linear optical
dual-polarization channel. We show that it can extend the application range of
blind adaptive equalizers by outperforming the state-of-the-art
constant-modulus algorithm (CMA) for PCS for both fixed but also time-varying
channels. The evaluation is accompanied with a hyperparameter analysis.Comment: Published (Open Access) in IEEE Journal on Selected Areas in
Communications, Sep 202
Neural-Network-Based Nonlinearity Equalizer for 128 GBaud Coherent Transcievers
We propose an efficient neural-network-based equalization jointly compensating fiber and transceiver nonlinearities for high-symbol-rate coherent short-reach links. Providing about 0.9 dB extra SNR gain, it allows achieving experimentally the record single-channel 1.48 Tbps net rate over 240 km G.652 fiber
Deep Neural Network-Based Digital Pre-distortion for High Baudrate Optical Coherent Transmission
High-symbol-rate coherent optical transceivers suffer more from the critical responses of transceiver components at high frequency, especially when applying a higher order modulation format. Recently, we proposed in [20] a neural network (NN)-based digital pre-distortion (DPD) technique trained to mitigate the transceiver response of a 128~GBaud optical coherent transmission system. In this paper, we further detail this work and assess the NN-based DPD by training it using either a direct learning architecture (DLA) or an indirect learning architecture (ILA), and compare performance against a Volterra series-based DPD and a linear DPD. Furthermore, we willfully increase the transmitter nonlinearity and compare the performance of the three DPDs considered. The proposed NN-based DPD trained using DLA performs the best among the three contenders, providing more than 1~dB signal-to-noise ratio (SNR) gains for uniform 64-quadrature amplitude modulation (QAM) and PCS-256-QAM signals at the output of a conventional coherent receiver DSP. Finally, the NN-based DPD enables achieving a record 1.61~Tb/s net rate transmission on a single channel after 80~km of standard single mode fiber (SSMF).</p
Rate Adaptation and Reach Increase by Probabilistically Shaped 64-QAM: An Experimental Demonstration
Single-channel 1.61 Tb/s optical coherent transmission enabled by neural network-based digital pre-distortion
We propose a novel digital pre-distortion (DPD) based on neural networks for high-baudrate optical coherent transmitters. We demonstrate experimentally that it outperforms an optimized linear DPD giving a 1.2 dB SNR gain in a 128GBaud PCS-256QAM single-channel transmission over 80km of standard single-mode fiber resulting in a record 1.61 Tb/s net data rate.Accepted Author ManuscriptTeam Sander Wahl
Deep Neural Network-Based Digital Pre-distortion for High Baudrate Optical Coherent Transmission
High-symbol-rate coherent optical transceivers suffer more from the critical responses of transceiver components at high frequency, especially when applying a higher order modulation format. Recently, we proposed in [20] a neural network (NN)-based digital pre-distortion (DPD) technique trained to mitigate the transceiver response of a 128~GBaud optical coherent transmission system. In this paper, we further detail this work and assess the NN-based DPD by training it using either a direct learning architecture (DLA) or an indirect learning architecture (ILA), and compare performance against a Volterra series-based DPD and a linear DPD. Furthermore, we willfully increase the transmitter nonlinearity and compare the performance of the three DPDs considered. The proposed NN-based DPD trained using DLA performs the best among the three contenders, providing more than 1~dB signal-to-noise ratio (SNR) gains for uniform 64-quadrature amplitude modulation (QAM) and PCS-256-QAM signals at the output of a conventional coherent receiver DSP. Finally, the NN-based DPD enables achieving a record 1.61~Tb/s net rate transmission on a single channel after 80~km of standard single mode fiber (SSMF).Accepted Author ManuscriptTeam Sander Wahl
54.5 Tb/s WDM Transmission over Field Deployed Fiber Enabled by Neural Network-Based Digital Pre-Distortion
We demonstrate a record 54.5 Tb/s WDM transmission at 11.35 bit/s/Hz over 48 km of field-deployed SMF connecting business and academic parks enabled by a novel joint I-Q Neural Network-based transmitter digital pre-distortion technique.Accepted Author ManuscriptTeam Sander Wahl