Interdimensional Interference Equalizing Using Recurrent Neural Network for Multi-Dimensional Optical Transmission

Abstract

During multi-dimensional optical transmission, because of photodetector (PD) operation as a square law detector, signals get distorted when they are modulated on polarization and phase due to interdimensional interference (IDI) by intensity-modulated signals. We propose an IDI mitigation technique using recurrent neural network for multi-dimensional optical transmission. The signal transmission performance of the proposed equalization technique was experimentally analyzed. The performance of the proposed system was verified using the symbol error rate (SER), eye diagram of the received multidimensional modulated signal.INDEX TERMS Multi-dimensional optical modulation, interdimensional interference (IDI), recurrent neural network (RNN), optical access network.

Wide and Deep Learning-Aided Nonlinear Equalizer for Coherent Optical Communication Systems

“…In Ref. [15], the authors proposed a recurrent neural network-based equalization technique that analyzes interdimensional interference to improve optical transmission performance. The results indicated the potential of a neural network-based equalizer to provide a new means of solving interference issues in multimode transmission systems.…”