Geometric Constellation Shaping with Low-complexity Demappers for Wiener Phase-noise Channels
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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.
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