Robust weighted K-means clustering algorithm for a probabilistic-shaped 64QAM coherent optical communication system

A novel weighted K-means scheme for a probabilistic-shaped (PS) 64 quadrature amplitude modulation (QAM) signal is proposed in order to locate the decision points more accurately and enhance the robustness of clustering algorithm. By using a weighting factor following the reciprocal of Maxwell-Boltzmann distribution, the proposed algorithm can combine the advantages of PS and K-means robustly while reducing the overall computational complexity of the clustering process. Experimental verification of the proposed clustering technique was demonstrated in a 120-Gb/s probabilistic-shaped 64QAM coherent optical communication system. The results show that the proposed algorithm has outperformed K-means with respect to bit error rate (BER), clustering robustness and iteration times in both back-to-back and 375km transmission scenarios. For the back-to-back situation, the proposed algorithm is capable of achieving about 0.6dB and 1.8dB OSNR gain over K-means clustered signals and unclustered signals. For the case of transmission, the proposed clustering procedure can robustly locate the optimal decision points with launched signal power ranging from −5dBm to 5dBm, while the working range for K-means procedure is only −4dBm to 2dBm. In addition, the proposed weighted algorithm takes less iteration times than K-means to converge, especially when the signal impairments caused by fiber Kerr nonlinearity is severe.