Geometrically shaped multidimensional constellations with more than 1028 points are simulated using fast and low-complexity algorithms without any look-up tables to store the constellation points. At the same symbol error rate, more than 78% and 114% reach improvement are demonstrated compared with 4- and 16-QAM, respectively
Using end-to-end deep learning, we experimentally demonstrate the optimized design of geometric cons...
We experimentally demonstrate high-cardinality, low-complexity Voronoi constellations based on the E...
Achievable information rates (AIRs) are discussed as a performance metric to design optimized geomet...
We introduce a simplified method for calculating the loss function for use in geometric shaping, all...
A scheme for the optimal shaping of multidimensional constellations is proposed. This scheme uses so...
Voronoi constellations (VCs) are finite sets of vectors of a coding lattice enclosed by the translat...
This paper presents design methods for highly efficient optimisation of geometrically shaped conste...
In a classical 1983 paper, Conway and Sloane presented fast encoding and decoding algorithms for a s...
This paper presents design methods for highly efficient optimisation of geometrically shaped constel...
The choice of constellations largely affects the performance of communication systems. When designin...
We propose Huffman-coded sphere shaping (HCSS) as a method for probabilistic constellation shaping w...
GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized...
We introduce and compare typical shaping schemes suitable for optical communications. The geometrica...
Gaussian channel inputs are required to achieve the capacity of additive white Gaussian noise (AWGN)...
Using end-to-end deep learning, we experimentally demonstrate the optimized design of geometric cons...
We experimentally demonstrate high-cardinality, low-complexity Voronoi constellations based on the E...
Achievable information rates (AIRs) are discussed as a performance metric to design optimized geomet...
We introduce a simplified method for calculating the loss function for use in geometric shaping, all...
A scheme for the optimal shaping of multidimensional constellations is proposed. This scheme uses so...
Voronoi constellations (VCs) are finite sets of vectors of a coding lattice enclosed by the translat...
This paper presents design methods for highly efficient optimisation of geometrically shaped conste...
In a classical 1983 paper, Conway and Sloane presented fast encoding and decoding algorithms for a s...
This paper presents design methods for highly efficient optimisation of geometrically shaped constel...
The choice of constellations largely affects the performance of communication systems. When designin...
We propose Huffman-coded sphere shaping (HCSS) as a method for probabilistic constellation shaping w...
GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized...
We introduce and compare typical shaping schemes suitable for optical communications. The geometrica...
Gaussian channel inputs are required to achieve the capacity of additive white Gaussian noise (AWGN)...
Using end-to-end deep learning, we experimentally demonstrate the optimized design of geometric cons...
We experimentally demonstrate high-cardinality, low-complexity Voronoi constellations based on the E...
Achievable information rates (AIRs) are discussed as a performance metric to design optimized geomet...
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