基于轻量化RF算法的高阶QAM信号OSNR估计方法

Estimation method of OSNR for high-order QAM signals based on lightweight RF algorithm

针对光信噪比(OSNR)估计复杂度高、计算量大的问题,提出了一种基于轻量化随机森林(RF)算法的高阶正交幅度调制(QAM)信号OSNR估计方法。该方法通过将不同OSNR的高阶QAM信号映射为不同的星座图数据集,并利用这些数据集来训练RF模型,从而实现OSNR的快速估计。仿真结果表明:采用基于轻量化RF算法估计64QAM和128QAM信号的OSNR,在系统OSNR真实值为5~30 d B时,2种调制格式的OSNR估计准确率均接近100%;64QAM信号OSNR估计值的平均绝对误差(MAE)为0.08 d B,128QAM的MAE为0.12 d B,比基于长短期记忆(LSTM)算法的信号OSNR估计结果更准确。

Aiming at the problems of high complexity and computational intensity in optical signal-to-noise ratio(OSNR)estima-tion,a high-order quadrature amplitude modulation(QAM)signal OSNR estimation method based on lightweight random forest(RF)algorithm is proposed.This method maps high-order QAM signals with different OSNRs into different constellation dia-gram datasets,and uses these datasets to train the RF model,thereby achieving rapid OSNR estimation.The simulation results show that when the real value of system OSNR is between 5~30 dB,the accuracy of OSNR estimation for 64QAM and 128QAM signals based on lightweight RF algorithm is close to 100%,the mean absolute error(MAE)of OSNR estimation for 64QAM sig-nals is 0.08 dB,and the MAE for 128QAM is 0.12 dB,which is more accurate than the signal OSNR estimation results based on long short-term memory(LSTM)algorithm.