150 citations · 864 across the 60 of their papers we have counts for
6 papers · 2 filters
BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling
Metodi Plamenov Yankov, Smaranika Swain, Ognjen Jovanovic +2
In this paper, a rate adaptive geometric constellation shaping (GCS) scheme which is fully backward-compatible with existing state of the art bit-interleaved coded modulation (BICM…
Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers
Sergio Hernandez, Ognjen Jovanovic, Christophe Peucheret +2
End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems. Such approach may require a differentiable channel…
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…
Rate Adaptive Geometric Constellation Shaping Using Autoencoders and Many-To-One Mapping
Metodi P. Yankov, Ognjen Jovanovic, Darko Zibar +1
A many-to-one mapping geometric constellation shaping scheme is proposed with a fixed modulation format, fixed FEC engine and rate adaptation with an arbitrarily small step. An aut…
Data-Driven Modeling of Directly-Modulated Lasers
Sergio Hernandez Fernandez, Christophe Peucheret, Ognjen Jovanovic +2
The end-to-end optimization of links based on directly-modulated lasers may require an analytically differentiable channel. We overcome this problem by developing and comparing dif…
Rate Adaptive Autoencoder-based Geometric Constellation Shaping
Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros +1
An autoencoder is used to optimize bit-to-symbol mappings for geometric constellation shaping. The mappings allow for net rate adaptivity without additional hardware complexity, wh…