Improving decryption quality of optical chaos communication using neural networks

Optical chaos communication is a promising secure transmission technique because of the advantages of high speed and compatibility with existing fiber-optic systems. The deterioration of chaotic synchronization quality caused by fiber optic transmission impairments affects the quality of recovery of information, especially high-order modulated signals. Here, we demonstrate that the use of a convolutional neural network (CNN) with a bidirectional long short-term memory (LSTM) layer can reduce the decryption BER in an optical chaos communication system based on common- signal-induced semiconductor laser synchronization. The performance of a neural network is investigated as a function of network parameters and chaos synchronization coefficient. Experimental results show that the BER of 16-ary quadrature-amplitude-modulation (16QAM) signal after 100-km fiber transmission is decreased from 3.05 x 10-2 to below the soft-decision forward-error-correction (SD-FEC) threshold of 2.0 x 10-2. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.