Transfer Learning Assisted Transmission Quality Evaluation based on Machine Learning

When the data distribution of optical fiber link changes, machine learning can be used to evaluate the transmission quality of link, which needs to recollect data and retrain. This process is time-consuming and complex. Transfer learning directly applies the previously learned knowledge to the current task, which requires less data. Therefore, it is proposed to use two transfer learning methods to realize multi classifier based on machine learning in the correlated optical communication system. The simulation results show that the multi classifier of machine learning combined with transfer learning and fine-tuning technology improves the multi classification index score by more than 0.25 compared with the direct transfer machine learning multi classifier. It is also shown that the impact of sample imbalance is reduced, and each category has high performance. It is proved that the machine learning multi classifier combined with transfer learning and fine-tuning technology can reduce the size of data set, reduce the cost of collecting data set, and improve the efficiency of transmission quality evaluation process in optical fiber link