Self-coherent detection for data centre
Grant number: DP190103724 | Funding period: 2019 - 2021
This project aims to explore the architecture of terabit data transport using self-coherent detection that addresses the tight constraints of power, space and cost in data centres. The project expects to create new knowledge in coherent detection based on optical equalisation rather than conventional power-hungry electronic equalisation. Expected outcomes of this project include advanced architecture of polarisation effect equalisers and all-optical equalisation algorithms as well as enhanced international collaboration with top experts in optical communications. The outcomes will contribute to maintaining Australia’s high reputation in the ICT arena.
Related publications (7)
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, William Shieh
We propose a novel, to the best of our knowledge, cascade recurrent neural network (RNN)-based nonlinear equalizer for a pulse amp..
Polarization-diversity receiver using remotely delivered local oscillator without optical polarization control.
Honglin Ji, Xian Zhou, Chuanbowen Sun, William Shieh
Silicon photonics coherent transceivers have integrated all the necessary optics except the lasers. The laser source has become a ..
Computational complexity comparison of feedforward/radial basis function/recurrent neural network-based equalizer for a 50-Gb/s PAM4 direct-detection optical link
Z Xu, C Sun, T Ji, JH Manton, W Shieh
The computational complexity and system bit-error-rate (BER) performance of four types of neural-network-based nonlinear equalizer..
High-dimensional Stokes vector direct detection over few-mode fibers
Honglin Ji, Di Che, Chuanbowen Sun, Jian Fang, Giovanni Milione, Ranjith Rajasekharan Unnithan, William Shieh
Direct detection attracts much attention for its simplicity compared with coherent detection. In this Letter, we propose for the f..