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Found 5 papers, 2 papers with code
End-to-end Optimization of Constellation Shaping for Wiener Phase Noise Channels with a Differentiable Blind Phase Search
As the demand for higher data throughput in coherent optical communication systems increases, we need to find ways to increase capacity in existing and future optical communication links.
Geometric Constellation Shaping with Low-complexity Demappers for Wiener Phase-noise Channels
We show that separating the in-phase and quadrature component in optimized, machine-learning based demappers of optical communications systems with geometric constellation shaping reduces the required computational complexity whilst retaining their good performance.
Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning
We propose a novel method to optimize the structure of factor graphs for graph-based inference.
Optimization of Geometric Constellation Shaping for Wiener Phase Noise Channels with Varying Channel Parameters
We present a novel method to investigate the effects of varying channel parameters on geometrically shaped constellations for communication systems employing the blind phase search algorithm.
Geometric Constellation Shaping for Phase-noise Channels Using a Differentiable Blind Phase Search
We perform geometric constellation shaping with optimized bit labeling using a binary autoencoder including a differential blind phase search (BPS).
