Unsupervised Learning for Neural Network-Based Blind Equalization

For the first time, an unsupervised learning training method was proposed for neural network (NN)-based blind equalization in intensity modulation and direct detection (IMDD) system. The whole scheme can directly train an NN-based equalizer with only the received signal rather than original symbols as reference. Besides, the unsupervised learning can also help a well-trained NN to keep its performance in face of varying system status, such as wavelength shift and bias fluctuation, in practical applications. We evaluate the performance of the proposed scheme in a 50 Gb/s IMDD system. The experimental results confirm that the proposed unsupervised learning can train fully-connected NN-based equalizer as well as convolutional neural network (CNN)-based equalizer to reach the same performance as the one trained by supervised learning. Besides, in face of signals with different bias current of the directly-modulated laser (DML), the unsupervised learning method can train the NN to keep the best performance. It can be proved that the scheme can help maintain the performance of NN-based equalizer against the continuous system parameter variations.