69 citations · 447 across the 41 of their papers we have counts for
30 papers · 1 filter
End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling
Shrinivas Chimmalgi, Laurent Schmalen, Vahid Aref
Probabilistic constellation shaping enables easy rate adaption and has been proven to reduce the gap to Shannon capacity. Constellation point probabilities are optimized to maximiz…
End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping
Vahid Aref, Mathieu Chagnon
We present a novel autoencoder-based learning of joint geometric and probabilistic constellation shaping for coded-modulation systems. It can maximize either the mutual information…
Successive Eigenvalue Removal for Multi-Soliton Spectral Amplitude Estimation
Alexander Span, Vahid Aref, Henning Buelow +1
Optical nonlinear Fourier transform-based communication systems require an accurate estimation of a signal's nonlinear spectrum, computed usually by piecewise approximation methods…
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/…
Spatially Coupled LDPC Codes with Non-uniform Coupling for Improved Decoding Speed
Laurent Schmalen, Vahid Aref
We consider spatially coupled low-density parity-check codes with finite smoothing parameters. A finite smoothing parameter is important for designing practical codes that are deco…