69 citations · 447 across the 41 of their papers we have counts for
47 papers
A Temporal Gaussian Noise Model for Equalization-enhanced Phase Noise
Benedikt Geiger, Fred Buchali, Vahid Aref +1
Equalization-enhanced Phase Noise causes burst-like distortions in high symbol-rate transmission systems. We propose a temporal Gaussian noise model that captures these distortions…
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…
A Novel Phenomenological Model of Equalization-enhanced Phase Noise
Benedikt Geiger, Fred Buchali, Vahid Aref +1
We show that equalization-enhanced phase noise manifests as a time-varying, frequency-dependent phase error, which can be modeled and reversed by a time-varying all-pass finite imp…
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…
Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication
Vladislav Neskorniuk, Andrea Carnio, Domenico Marsella +3
Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation sha…
Efficient Training of Volterra Series-Based Pre-distortion Filter Using Neural Networks
Vinod Bajaj, Mathieu Chagnon, Sander Wahls +1
We present a simple, efficient "direct learning" approach to train Volterra series-based digital pre-distortion filters using neural networks. We show its superior performance over…