An optimized LSTM-based equalizer for 100 Gigabit/s-class short-range fiber-optic communications - Vuong Quang Phuoc; Nguyen Van Dien; Ho Duc Tam Linh; Nguyen Van Tuan; Nguyen Van Hieu; Le Thai Son; Nguyen Tan Hung (AIMS Electronics and Electrical Engineering)
Intensity modulation/direct detection (IM/DD) remains to be the preferred optical transmission scheme for short-range applications for its simplicity of application, inexpensiveness, and small footprint. However, the impairments of low-cost device and fiber chromatic dispersion lead to the limitation of system performance when the data rate rises to 100 Gbps or higher. In this paper, we demonstrated that an equalizer using neural networks can effectively improve the transmission performance of high-speed IM/DD systems. An optimization of a long short-term memory (LSTM) structure in terms of network depth and distribution of neurons in hidden layers leads to an enhancement of the overall performance of the 50 Gbaud PAM4 communications. Furthermore, the results for a system using a LSTM-based equalizer give the better outcome than the traditional feed-forward equalizer (FFE) or artificial neural network (ANN)-based equalizer.