NEC And Google Speed Up trans-Pacific system
NEC Corporation, in a joint research publication with Google, has demonstrated for the first time that the FASTER open subsea cable can be upgraded to a spectral efficiency of 6 bits/s per Hertz (bits/s/Hz) in an 11,000 km segment. This represents a capacity of more than 26 Tbits/s in the C-band, which is over 2½ times the capacity originally planned for the cable. Importantly, there’s no additional wet plant capital expenditure.
The team achieved this result using near-Shannon probabilistic-shaping at a modulation of 64 QAM and, for the first time on a live cable, Artificial Intelligence (AI) was used to analyse data for the purpose of NonLinearity Compensation (NLC). NEC developed an NLC algorithm based on data-driven Deep Neural Networks (DNN) to accurately and efficiently estimate the signal nonlinearity.
“Other approaches to NLC have attempted to solve the nonlinear Schrodinger equation, which requires the use of very complex algorithms,” said NEC’s Toru Kawauchi, General Manager, Submarine Network Division. “This approach sets aside those deterministic models of nonlinear propagation, in favour of a low-complexity black-box model of the fibre, generated by machine learning algorithms. The results demonstrate both an improvement in transmission performance and a reduction in implementation complexity.”
“Furthermore, since the black-box model is built up from live transmission data, it does not require advance knowledge of the cable parameters,” continued Kawauchi. “This allows the model to be used on any cable without prior modelling or characterisation, which shows the potential application of AI technology to open subsea cable systems, on which terminal equipment from multiple vendors may be readily installed.”
The experimental demonstration of NLC achieved a Generalised Mutual-Information (GMI) capacity gain of ~0.15 bits/s/2-pol, which is equivalent to a capacity increase of 15 Gbits/s in every 100 GHz of fibre bandwidth. NEC announced plans to continue this AI-based research, with the dual aims of increasing system capacity while reducing the complexity of implementation.
The research publication can be downloaded here.