Joint learning of geometric and probabilistic constellation shaping
The choice of constellations largely affects the performance of communication systems.
When designing constellations, both the locations and probability of occurrence of the points …
When designing constellations, both the locations and probability of occurrence of the points …
Joint learning of probabilistic and geometric shaping for coded modulation systems
We introduce a trainable coded modulation scheme that enables joint optimization of the bit-
wise mutual information (BMI) through probabilistic shaping, geometric shaping, bit labeling …
wise mutual information (BMI) through probabilistic shaping, geometric shaping, bit labeling …
End-to-end learning of joint geometric and probabilistic constellation shaping
We present a novel autoencoder-based learning of joint geometric and probabilistic
constellation shaping for coded-modulation systems. It can maximize either the mutual …
constellation shaping for coded-modulation systems. It can maximize either the mutual …
Geometric constellation shaping for fiber optic communication systems via end-to-end learning
In this paper, an unsupervised machine learning method for geometric constellation shaping
is investigated. By embedding a differentiable fiber channel model within two neural …
is investigated. By embedding a differentiable fiber channel model within two neural …
End-to-end learning of geometrical shaping maximizing generalized mutual information
GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient
descent initialized with Gray-labeled APSK constellations directly to the constellation …
descent initialized with Gray-labeled APSK constellations directly to the constellation …
End-to-end learning of a constellation shape robust to variations in SNR and laser linewidth
We propose an autoencoder-based geometric shaping that learns a constellation robust to
SNR and laser linewidth estimation errors. This constellation maintains shaping gain in …
SNR and laser linewidth estimation errors. This constellation maintains shaping gain in …
End-to-end learning for GMI optimized geometric constellation shape
Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings.
Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the …
Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the …
Deep learning constellation design for the AWGN channel with additive radar interference
F Alberge - IEEE Transactions on Communications, 2018 - ieeexplore.ieee.org
Radar and wireless communication coexistence is considered in this paper as a possible
solution to face the exploding demand and rising congestion in wireless networks. The …
solution to face the exploding demand and rising congestion in wireless networks. The …
Prefix-free code distribution matching for probabilistic constellation shaping
J Cho - IEEE Transactions on Communications, 2019 - ieeexplore.ieee.org
In this paper, we construct variable-length prefix-free codes that are optimal (or near-
optimal) in the sense that no (or few) other codes of the same cardinality can achieve a …
optimal) in the sense that no (or few) other codes of the same cardinality can achieve a …
End-to-end learning of a constellation shape robust to channel condition uncertainties
Vendor interoperability is one of the desired future characteristics of optical networks. This
means that the transmission system needs to support a variety of hardware with different …
means that the transmission system needs to support a variety of hardware with different …