为应对概率整形场景下相干光通信系统中的偏振解复用问题,提出了一种基于独立成分分析和极大似然估计的偏振解复用算法。由于各个信号之间相互独立,因此可以对信号采用独立成分分析的手段进行偏振解复用。通过基于最大似然估计的迭代更新寻找最佳的分离矩阵,即偏振解复用矩阵。对所提算法在不同信噪比下的性能及整形强度的容忍度进行了仿真分析。结果表明,所提算法能够应对不同的概率整形强度,在较大的信噪比范围内均能完成良好的偏振解复用。相较于用于标准信号的恒模算法,所提算法并不会受到整形强度的影响,并且随着整形强度的增加,系统的性能有所提升。

To deal with the problem of polarization demultiplexing in coherent optical communication systems for probabilistically shaped scenes, this paper proposes a polarization demultiplexing algorithm based on independent component analysis and maximum likelihood estimation. Due to the independence between the signals, the polarization demultiplexing of signals can be realized by the independent component analysis. The iterative update based on maximum likelihood estimation is used to find the best separation matrix, which is the polarization demultiplexing matrix. The performance of the algorithm under different optical signal-to-noise ratios and the tolerance of the shaping intensity are simulated and analyzed. The results show that the proposed scheme can cope with different probabilistically shaped intensities, and can achieve good polarization demultiplexing in a large optical signal-to-noise ratio range. Compared with the constant modulus algorithm for standard signals, this scheme will not be affected by the shaping intensity. As the shaping intensity increases, the performance of the system can be improved.