For the first time, to the best of our knowledge, we experimentally demonstrate a successful 30-Gb/s signal transmission of a duobinary message hidden in a chaotic optical carrier over 100-km fiber. Thanks to the duobinary modulation format with high spectral efficiency, the 30-Gb/s signal can be encrypted by a 10-GHz-wide chaotic carrier. A digital signal processing technique can be used to convert duobinary data into binary data on the receiver side. This proposal lowers the requirement for wideband chaos generation and synchronization in high-speed long-distance chaotic optical communications, and fiber dispersion compensation can also be simplified, which has potential to be used in high-speed long-distance secure optical communications.

Nonlinear polarization evolution-based passively mode-locked fiber lasers with ultrafast and high peak power pulses are a powerful tool for engineering applications and scientific research. However, their sensitivity to polarization limits their widespread application. To address this, automatic mode-locking immune to environmental disturbances is gaining attention. Here, we experimentally demonstrate the first intelligent programmable mode-locked fiber laser enabled by our proposed human-like algorithm, combining the human approach with machine speed, computing capability, and precision. The laser is capable of automatically locking onto multiple operation regimes, such as fundamental mode-locking, harmonic mode-locking, $Q$-switching, and even $Q$-switched mode-locking without physically altering its structure. The shortest initial mode-locking time and recovery time from detachment are only 0.22 s and 14.8 ms, respectively, which are the record values to date. We believe this intelligent laser with superior performance can find practical applications in engineering and provide infinite possibilities for scientific research.

In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function. Our investigation focuses on joint channel effects of attenuation, chromic dispersion, self-phase modulation (SPM), and amplified spontaneous emission (ASE) noise. To achieve the success of GAN for channel modeling, we modify the loss function, design the condition vector of input and address the mode collapse for the long-haul transmission. The effective architecture, parameters, and training skills of GAN are also displayed in the paper. The results show that the proposed method can learn the accurate transfer function of the fiber channel. The transmission distance of modeling can be up to 1000 km and can be extended to arbitrary distance theoretically. Moreover, GAN shows robust generalization abilities under different optical launch powers, modulation formats, and input signal distributions. Comparing the complexity of GAN with the split-step Fourier method (SSFM), the total multiplication number is only 2% of SSFM and the running time is less than 0.1 seconds for 1000-km transmission, versus 400 seconds using the SSFM under the same hardware and software conditions, which highlights the remarkable reduction in complexity of the fiber channel modeling.

Chaotic optical communications were originally proposed to provide high-level physical layer security for optical communications. Limited by the difficulty of chaos synchronization, there has been little experimental demonstration of high-speed chaotic optical communications, and point to multipoint chaotic optical networking is hard to implement. Here, we propose a method to overcome the current limitations. By using a deep-learning-based scheme to learn the complex nonlinear model of the chaotic transmitter, wideband chaos synchronization can be realized in the digital domain. Therefore, the chaotic receiver can be significantly simplified while still guaranteeing security. A successful transmission of 32 Gb/s messages hidden in a wideband chaotic optical carrier was experimentally demonstrated over a 20 km fiber link. We believe the proposed deep-learning-based chaos synchronization method will enable a new direction for further development of high-speed chaotic optical communication systems and networks.

Deep neural networks usually process information through multiple hidden layers. However, most hardware reservoir computing recurrent networks only have one hidden reservoir layer, which significantly limits the capability of solving practical complex tasks. Here we show a deep photonic reservoir computing (PRC) architecture, which is constructed by cascading injection-locked semiconductor lasers. In particular, the connection between successive hidden layers is all optical, without any optical-electrical conversion or analog-digital conversion. The proof of concept PRC consisting of 4 hidden layers and a total of 320 interconnected neurons (80 neurons per layer) is demonstrated in experiment. The deep PRC is applied in solving the real-world problem of signal equalization in an optical fiber communication system. It is found that the deep PRC exhibits strong capability in compensating for the nonlinear impairment of optical fibers.