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.
翻译:我们表明,将以几何星座形状的光学通信系统的优化、基于机器学习的离子体分离成的中相和二次构件,在保持其良好性能的同时,减少了所需的计算复杂性。