PFA-NS: Power-Fading-Aware Noise Shaping Enabled C-Band IMDD System with Low Resolution DAC

We propose and demonstrate a power-fading-aware noise-shaping technique for C-band IMDD system with low resolution DAC, which shapes and concentrates quantization noise within the fading-induced notch areas, yielding 94% improvement in data-rate over traditional counterpart.

EDA-RoF: Elastic Digital-Analog Radio-Over-Fiber (RoF) Modulation and Demodulation Architecture Enabling Seamless Transition Between Analog RoF and Digital RoF

We propose and demonstrate an elastic digital-analog radio-over-fiber (RoF) modulation and demodulation architecture, seamlessly bridging A-RoF and D-RoF solutions, achieving quasilinear SNR scaling with respect to 1/η, and evidenced by R^2=0.9908.

Multicarrier Modulation-Based Digital Radio-over-Fibre System Achieving Unequal Bit Protection with Over 10 dB SNR Gain

We propose a multicarrier modulation-based digital radio-over-fibre system achieving unequal bit protection by bit and power allocation for subcarriers. A theoretical SNR gain of 16.1 dB is obtained in the AWGN channel and the simulation results show a 13.5 dB gain in the bandwidth-limited case.

Application of Machine Learning in Fiber Nonlinearity Modeling and Monitoring for Elastic Optical Networks

Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and investigated separately. Moreover, the accuracy of the previously proposed methods still needs to be improved for heterogenous dynamic optical networks. In this paper, we present the application of machine learning (ML) in NLI modeling and monitoring. In particular, we first propose to use ML approaches to calibrate the errors of current fiber nonlinearity models. The Gaussian-noise (GN) model is used as an illustrative example, and significant improvement is demonstrated with the aid of an artificial neural network (ANN). Further, we propose to use ML to combine the modeling and monitoring schemes for a better estimation of NLI variance. Extensive simulations with 1603 links are conducted to evaluate and analyze the performance of various schemes, and the superior performance of the ML-aided combination of modeling and monitoring is demonstrated.