100 citations · 128 across the 7 of their papers we have counts for
7 papers
Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications
Boris Karanov, Mathieu Chagnon, Vahid Aref +4
We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN) for communication over dispersive nonlinear channels. In…
Optical Fiber Communication Systems Based on End-to-End Deep Learning
Boris Karanov, Mathieu Chagnon, Vahid Aref +3
We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. In particular, we report…
Experimental Demonstration of Learned Time-Domain Digital Back-Propagation
Eric Sillekens, Wenting Yi, Daniel Semrau +9
We present the first experimental demonstration of learned time-domain digital back-propagation (DBP), in 64-GBd dual-polarization 64-QAM signal transmission over 1014 km. Performa…
Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model
Boris Karanov, Mathieu Chagnon, Vahid Aref +3
We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel. Previously, optimizatio…
Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing
Boris Karanov, Gabriele Liga, Vahid Aref +3
In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IM/…
End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
Boris Karanov, Domaniç Lavery, Polina Bayvel +1
We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional…