Fan Xiaoqi, Mao Xiaoxin, Wang Longsheng, Fu Songnian, Wang Anbang, Wang Yuncai
Opt Lett. 2024 Aug 1;49(15):4445-4448. doi: 10.1364/OL.531834.
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 × 10 to below the soft-decision forward-error-correction (SD-FEC) threshold of 2.0 × 10.
光学混沌通信由于具有高速以及与现有光纤系统兼容的优点，是一种很有前景的安全传输技术。光纤传输损伤导致的混沌同步质量恶化会影响信息恢复的质量，尤其是高阶调制信号。在此，我们证明，在基于共信号诱导半导体激光同步的光学混沌通信系统中，使用具有双向长短期记忆（LSTM）层的卷积神经网络（CNN）可以降低解密误码率。研究了神经网络性能与网络参数和混沌同步系数的函数关系。实验结果表明，100公里光纤传输后16进制正交幅度调制（16QAM）信号的误码率从3.05×10降至低于2.0×10的软判决前向纠错（SD-FEC）阈值。
