A chaotic optical communication scheme assisted by artificial intelligence (AI) method is proposed. Different from other traditional solutions, the chaos synchronization of the proposed scheme is accomplished via an AI-based optoelectronic oscillator (AI-OEO) model and a QPSK driving signal distorted by chromatic dispersion. Specifically, the distorted driving signal excites the chaotic AI-OEO model to generate the chaotic signal for encryption and decryption. One significant advantage is that the strict dependence of chaos synchronization on physical devices can be reduced by using deep learning technology to model the actual experimental chaotic system. The proposed scheme has been experimentally validated in a chaotic-encrypted 256Gbit/s (32-GBaud) polarization-multiplexed 16QAM system over 1600km standard single-mode fiber (SSMF). The results show that the BER performance after 1600km transmission is lower than the 20% FEC threshold limit (BER = 2.4×10 ^-2 ). Furthermore, we conducted a comprehensive security assessment of the system, comparing it with traditional chaotic encryption schemes and performing a detailed analysis of the key space and key sensitivity. The proposed scheme is compatible with the existing high-speed coherent optical fiber communication systems, and the realization of precise chaos synchronization only requires ensuring that the driving signal is decoded without errors at the receiver. We believe that it has the potential to become a candidate solution for bidirectional high-speed secure communication with low complexity and cost.

In this paper, a novel deep learning architecture, Fourier neural operator (FNO), has been introduced to ap-proximate the nonlinear Schrodinger equation which characterizes fiber transmission impairments such as fiber attenuation, chromatic dispersion, nonlinear impairments, etc. The proposed scheme is firstly verified via numerical simulation in 28-GBaud 4QAM/16QAM/64QAM systems over 1200-km standard single mode fiber (SSMF) under different launch powers. The simulation results show that the mean square errors (MSEs) are less than 0.002 and the effective SNRs differences between the proposed FNO and SSFM are all within 1 dB at 1200 km SSMF with the launching power from −5 dBm to 5 dBm. The performance of the proposed FNO model is further evaluated by a conceptual experiment. The experimental results show that the Q-factor performance of the FNO-based channel model is close to that of 1200 km experimental SSMF transmission link with the launching power from −3 dBm to 5 dBm.

We propose a M-QAM chaotic optical communication system under amplitude and phase encryption. In the legitimate transmitter (Alice), we first design an electro-optic nonlinear delay chaotic source which can provide chaotic optical intensity and phase carriers under ∼15GHz bandwidth. The M-QAM plaintext signal is masked by both chaotic optical intensity and phase carriers for ensuring the safety of intensity and phase information of M-QAM signal. In the legitimate receiver (Bob), the chaotic encrypted M-QAM signal after chromatic dispersion compensation by dispersion compensation fiber is decrypted by open-loop chaotic synchronization operation. Subsequently, the decrypted signal is processed by offline digital signal processing (DSP) module for impairments equalization. The proposed scheme is numerically demonstrated in an 84Gbit/s 8QAM chaotic optical communication system over 1100km standard single-mode fiber (SSMF), and a 112Gbit/s 16QAM chaotic optical communication system over 700km SSMF. We believe that the proposed scheme represents a promising strategy for enhancing the security of high-speed and long-haul optical communication system.

By adding output feedback and a sliding window, a software-based enhanced reservoir computing (RC) is proposed and verified in intensity modulation direct detection (IMDD)-based short-reach optical transmission systems. Both simulation and experiment confirm the enhanced RC is effective for dispersion and nonlinearity compensation for short-reach IMDD systems. Moreover, compared with the original RC without output feedback and sliding window, the enhanced RC can reduce the required received optical power (ROP) by 2 dB at 7% FEC limit, and decrease reservoir layer neuron number from 1800 to 300 without jeopardizing the performance within 25 km transmission. In addition, traditional DSP equalizers, including feed-forward equalizer (FFE), decision feedback equalizer (DFE), Volterra equalizer and neuron network equalizers, such as feedforward neural network (FNN), long-short-term memory network (LSTM) and bidirectional LSTM (BiLSTM), are conducted for a thorough comparison with the proposed RC equalizer. The corresponding experimental results show that the enhanced RC demonstrates lower BER and longer transmission distance with a smaller sliding window size.

An intelligent optical performance monitoring scheme for simultaneous modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring is proposed and experimentally demonstrated in digital coherent receivers. The proposed scheme introduces convolutional neural network (CNN) to automatically extract and monitor modulation format and OSNR dependent features (the empirical distribution of intensity and differential-phase) which can be obtained after constant modulus algorithm (CMA) equalization. The experiment results show that 100% identification accuracies for all modulation formats (e.g. 28-GBaud PDM-QPSK/-8PSK/ -8QAM/-16QAM/-32QAM/-64QAM) are achieved at OSNR values are lower than the corresponding theoretical 20% FEC limit (BER=2.4×10-2). Furthermore, under the chromatic dispersion (CD) variation from 0-ps/nm to 1200-ps/nm, the root-mean- square error (RMSE) and mean absolute error (MAE) of OSNR monitoring for all modulation formats are less than 0.0896-dB and 0.0657-dB, respectively. Subsequently, the influence of frequency offset and fiber nonlinearity effect on the intelligent optical performance monitoring scheme is also analyzed. We believe that our proposed multi-parameter monitoring scheme has the potential to be applied in the next-generation intelligent elastic optical networks.

According to different transmission distances, application scenarios of a data center mainly include intra- and inter-data center optical interconnects. The intra-data center optical interconnect is considered as a few kilometers optical interconnect between servers and racks inside a data center, which accounts for nearly 80% of data traffic of a data center. The other one, inter-data center optical interconnect, is mainly applied in tens of kilometers data transmission among different data centers. Since data exchange in data centers generally occurs between many servers and racks, and a lot of transmitter and receiver components are required, optical interconnects become highly sensitive to component costs. In this paper, we firstly review the development and applications of mainstream transmitter components (e.g., VCSEL, DML, EML, MZM, and monolithic integrated transmitter) and receiver components (e.g., single-end photodetector, Kramers-Kronig receiver, Stokes vector receiver, and monolithic integrated receiver), which have been widely applied in short-reach transmission systems. Then, two types of integrated solutions including simplified detection scheme and transceiver integration scheme are presented in detail. Finally, we summarize and discuss the technological and component options for different transmission distances. We believe that monolithic integrated components, especially transceiver integration, will become a powerful solution for next-generation high-speed short-reach transmission systems.