Xiaobo Zeng

Xiaobo Zeng, Liangcai Chen, Pan Liu et al.

Cost-effective, low-complexity and spectrally efficient interconnection can offer fundamental guiding law for future datacenter. In this work, we demonstrate a cost-efficient SSBI-free direct detection for datacenter interconnection, leveraging the phase diversity of residual optical carrier caused by finite-extinction ratio (ER) IQ modulators, combining the device cost-effective IQ modulator with finite-ER and efficient SSBI-free phase-diverse direct detection receiver. Specifically, the proposed solution transforms the inherent limitation of finite-ER of cost-effective IQ modulator into the residual optical carrier advantage of SSBI-free direct detection systems, eliminating SSBI without additional hardware and control complexity. A digital pre-distortion and offset correction algorithms, and a PD-thermal-noise constrained SSBI-free direct detection and signal recovery algorithms are derived and implemented. Comprehensive simulations are conducted. A Global-SNR gain of 1.78 dB and 400 Gb/s data rate are achieved in 100-km SSMF transmission when (ER_i, ER_o)= (7 dB, 25 dB) of IQ modulator. The proposed solution enables low-complexity, cost-effective, and spectrally-efficient interconnects for next-generation datacenters.

Qunbi Zhuge, Xiaobo Zeng, Huazhi Lun et al.

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. The following contents are the listed errors as mentioned in the comments for reasons of withdrawal. (1) The works, as mentioned as the title, should be addressed is about the elastic optical networks(EON), however, the simulation setup and the results section are focused on the conventional wavelength division multiplexing(WDM) networks. This error may confuse some researcher, getting the misleading decision for the researches about the elastic optical networks. (2) There exists some errors in the results rection, such as, Fig.9(b) and (c) with the wrong captions may result in misleading decision. (3) The split-step-Fourier-method(SSFM) presents good accuracy if the sufficiently small steps are adopted in the calculation, however this paper has not necessary contents and efforts to optimise the step-length of SSFM. This error may confuse the accuracy of simulation results. Therefore, we decide to withdraw this paper from arXiv. The correct and complete paper with the same title was published in journal of lightwave technology with doi: 10.1109/JLT.2019.2910143.