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.