We propose a joint look-up-table (LUT)-based nonlinear predistortion and digital resolution enhancement scheme to achieve high-speed and low-cost optical interconnects using low-resolution digital-to-analog converters (DACs). The LUT-based predistortion is employed to mitigate the pattern-dependent effect (PDE) of a semiconductor optical amplifier (SOA), while the digital resolution enhancer (DRE) is utilized to shape the quantization noise, lowering the requirement for the resolution of DAC. We experimentally demonstrate O-band intensity modulation and direct detection (IM/DD) transmission of 124-GBd 4 / 6-level pulse-amplitude modulation ( PAM ) -4 / 6 and 112-GBd PAM-8 signals over a 2-km standard single-mode fiber (SSMF) with 3 / 3.5 / 4-bit DACs. In the case of 40-km SSMF transmission with an SOA-based preamplifier, 124-GBd on-off-keying (OOK)/PAM-3/PAM-4 signals are successfully transmitted with 1.5 / 2 / 3-bit DACs. To the best of our knowledge, we have achieved the highest net data rates of 235.3-Gb / s PAM-4, 289.7-Gb / s PAM-6, and 294.7 Gb / s PAM-8 signals over 2-km SSMF, as well as 117.6-Gb / s OOK, 173.8-Gb / s PAM-3, and -231.8 Gb / s PAM-4 signals over 40-km SSMF, employing low-resolution DACs. The experimental results reveal that the joint LUT-based predistortion and DRE effectively mitigate the PDE and improve the signal-to-quantization noise ratio by shaping the noise. The proposed scheme can provide a powerful solution for low-cost IM/DD optical interconnects beyond 200 Gb / s.

A designed visual geometry group (VGG)-based convolutional neural network (CNN) model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements. Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format, probabilistic shaping, roll-off factor, baud rate, optical signal-to-noise ratio, and chromatic dispersion. The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios. Furthermore, the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network. Compared with the designed VGG-based CNN, the MobileNet-based MTL does not need to train all the classes, and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy, indicating great potential in various monitoring scenarios.

We propose an encryption technique for underwater visible light communication (UVLC) based on chaotic phase scrambling (PS) and conjugate frequency hopping (CFH). The technique is experimentally tested using an 8-level pulse amplitude modulation (PAM-8) and a 1.2 m underwater link. The security key of the phase scrambling code is generated according to a logistic map, and the frequency hopping is achieved by adding the same zero frequency points to the signal spectrum. The maximum transmission rate of 2.1 Gbit/s is measured with bit-error-rate (BER) below 7% the hard-decision forward error correction (HD-FEC) threshold of 3.8×10-3.

Probabilistically shaped (PS) pulse amplitude modulation (PAM) is a promising technique for intra-data-center networks due to its superior performance, for which a low-complexity and cost-effective distributed matching method is critical. In this work, we propose an energy-level-assigned method to yield PS-PAM-4 signals with various bit rates based on variable probabilistic distributions. We experimentally demonstrate the proposed method in a 25 Gbaud PS-PAM-4 transmission over a bandwidth of approximately 10 GHz. Compared to a uniform PAM-4 system, the proposed multi-distributed PS-PAM-4 system approaches the hard decision threshold at a wide range of received optical power for different applications.