NEC and Google leverage AI to boost subsea cable capacity

subsea cable
Image credit: Vismar UK / Shutterstock.com

NEC says it has carried out successful transmission tests with Google over a commercial long-haul subsea cable in which it more than doubled capacity using artificial intelligence (AI) and probabilistic shaping at a modulation of 64QAM.

NEC, in a joint research publication with Google, demonstrated for the first time that the FASTER trans-pacific open subsea cable can be upgraded to a spectral efficiency of 6 bits per second per hertz (b/s/Hz) in an 11,000-km segment. This represents a capacity of more than 26 Tbps in the C-band, which is over 2.5 times the capacity originally planned for the cable, for no additional wet plant capital expenditure.

In doing so, the authors set a spectral efficiency-distance product record of 66,102 b/s/Hz in a field trial performed together with live traffic neighboring channels.

The team achieved this result using near-Shannon probabilistic-shaping at a modulation of 64QAM. Also, for the first time on a live cable, AI was used to analyze data for the purpose of nonlinearity compensation (NLC). NEC says it 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 Toru Kawauchi, general manager of NEC’s Submarine Network division. “This approach sets aside those deterministic models of nonlinear propagation, in favor of a low-complexity black-box model of the fiber, generated by machine learning algorithms.”

He added that 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,” Kawauchi continued. “This allows the model to be used on any cable without prior modeling or characterization, which shows the potential application of AI technology to open subsea cable systems, on which terminal equipment from multiple vendors may be readily installed.”

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