Abstract

The cloud edge data center will enable reliable and low latency options for the network, and the interconnection among these data-centers will demand a scalable low-complexity scheme. An intensity-modulated and directed detected transmission system is an attractive solution, but chromatic dispersion is the main limitation for higher symbol rate systems. To overcome this challenge, we have proposed and experimentally demonstrated a receiver with shared-complexity between optical and digital domains that enables 80 km transmission reach below KP4 FEC limit for a 32 GBd on-off keying signal. The optical stage consists of optical filters that slices the signal into smaller sub-bands and each is detected by a photodetector. A feedforward neural network and reservoir computing are compared to reconstruct the full signal from the slices and mitigate the chromatic dispersion. Both equalizers have shown similar performance with the advantage of the reservoir computing requiring fewer inputs and easier training process. In this work, we have compared the linear and nonlinear activation functions in the feedforward neural network to investigate the gain of using a nonlinear equalizer. The maximum transmission reach is reduced almost to half, $\approx$ 45 km, when using the linear. The performance is also reduced if a reduced number of slices is used in the receiver, as we have demonstrated. In this case, using 2 slices to reduce the complexity of the system, instead of the total 4, we have shown a $\approx$ 55 km transmission reach below KP4 FEC limit. In this work we have also provided a numerical comparison with 4x8 GBd subcarriers system. The results have shown a 40 km increase in transmission reach compared to the proposed optoelectronic system. The trade-off between performance and complexity should be analyzed for each case, as a different hardware is required in each situation.

Optical amplification-free deep reservoir computing-assisted high-baudrate short-reach communication

Mengyao Han, Muguang Wang, Yuchuan Fan, Toms Salgals, Hadrien Louchet, Richard Schatz, Markus Gruen, Fabio Pittala, Benjamin Krüger, Thomas Dippon, Lu Zhang, Xianbin Yu, Sandis Spolitis, Vjaceslavs Bobrovs, Sergei Popov, Xiaodan Pang, and Oskars Ozolins
Opt. Lett. 48(8) 2122-2125 (2023)

Experimental realization of integrated photonic reservoir computing for nonlinear fiber distortion compensation

Stijn Sackesyn, Chonghuai Ma, Joni Dambre, and Peter Bienstman
Opt. Express 29(20) 30991-30997 (2021)

Reservoir computing system with double optoelectronic feedback loops

Yaping Chen, Lilin Yi, Junxiang Ke, Zhao Yang, Yunpeng Yang, Luyao Huang, Qunbi Zhuge, and Weisheng Hu
Opt. Express 27(20) 27431-27440 (2019)

Cascade recurrent neural network-assisted nonlinear equalization for a 100 Gb/s PAM4 short-reach direct detection system

Zhaopeng Xu, Chuanbowen Sun, Tonghui Ji, Jonathan H. Manton, and William Shieh
Opt. Lett. 45(15) 4216-4219 (2020)

Virtual reservoir computer using an optical resonator

Somayeh Boshgazi, Ali Jabbari, Khashayar Mehrany, and Mohammad Memarian
Opt. Mater. Express 12(3) 1140-1153 (2022)