Periodic Reporting for period 2 - FONTE (Fibre optic nonlinear technologies)
Okres sprawozdawczy: 2020-06-01 do 2022-05-31
It is hard to overstate the impact that fibre-optic networks, which are the backbone of the global communication infrastructure, are making on society, economy, healthcare, government services and on almost all aspects of our lives. This became even more clear during the COVID-19 pandemic. The >2 billion km of optical fibre deployed worldwide carry most of the world's information traffic, enabling Internet and digital economy. Yet the exponential surge in global data traffic, driven by the proliferation of on-line services, is quickly pushing the current generation of optical fibre communication system towards the limits of modern technology.
The nonlinear transmission effects in optical fibre are among major limiting factors in modern optical communication systems. There is a clear need for radically different methods for the coding, transmission, and processing of information that take the nonlinear properties of the optical fibre into account, and for the training of a new generation of engineers with expertise in both optical communications and nonlinear science.
The FONTE European Industrial Doctorate is a research & training network of internationally leading teams from academia and industry dealing with these important challenges. Research in FONTE led to development of disruptive techniques for fibre-optic communications beyond the limits of current technology by constructively exploiting the inherent nonlinearity of optical fibre using advanced digital signal processing. The industry-focused R&D tasks were carried out along with training of PhD students in leading research centres in Europe, including >18 month spent in the world leading telecom centre Nokia Bell Labs (Germany).
The nonlinear transmission effects in optical fibre are among major limiting factors in modern optical communication systems. There is a clear need for radically different methods for the coding, transmission, and processing of information that take the nonlinear properties of the optical fibre into account, and for the training of a new generation of engineers with expertise in both optical communications and nonlinear science.
The FONTE European Industrial Doctorate is a research & training network of internationally leading teams from academia and industry dealing with these important challenges. Research in FONTE led to development of disruptive techniques for fibre-optic communications beyond the limits of current technology by constructively exploiting the inherent nonlinearity of optical fibre using advanced digital signal processing. The industry-focused R&D tasks were carried out along with training of PhD students in leading research centres in Europe, including >18 month spent in the world leading telecom centre Nokia Bell Labs (Germany).
FONTE has produced a number of research results at the edge of modern communication technology. FONTE engineered cost-effective deep learning solutions for improving optical communication links. Among the most important results we would like to mention:
• Achieved a world record transmission rate of 1.48 Tbps over a typical inter-data-centre link through developing and implementing a deep-neural-network-based de-noiser.
• Improved transmission quality by 5× in a real-world operational 600 km link by developing and implementing a tailored complex-value-based neural network.
• Increased the capacity by 4% at no additional cost in a 2400 km link by learning the optimal link configuration via an autoencoder-based machine learning algorithm.
• Trimmed by 2× training complexity of time-series filtering algorithms and by 6× the required dataset size via developing a data augmentation procedure.
• We developed a novel approach to incorporating loss into the nonlinear Fourier transform based systems exploiting the dispersion decreasing fibres. The proposed solution substantially suppresses the degrading effects of fibre loss.
Together with industrial partner Nokia Bell Labs we proposed and developed a new receiver based on optoelectronic machine learning for intensity-modulated and direct detection systems. The optical pre-processing stage slices the received signal spectrum in small sub-bands with passive optical filters and each is detected by a photodetector with a digital post-processing based on reservoir computing. The method led to a patent filed by the Nokia Bell Labs.
• We proposed and numerically demonstrated the potential of the receiver for 32-GBd OOK signal transmission, showing an increase in reach from 10 km to 40 km, corresponding to 400%, compared with digital-only techniques.
• We validated these findings in an experimental setting at the industry partner facilities. The proposed receiver together with ML-based equalizers enabled to increase the transmission reach from 10 km to more than 80 km, i.e. a more than 800% increase in reach.
To alleviate nonlinear channel impairments in long-haul fibre-optic communications, we developed a neural network equalizer consisting of a convolutional neural network encoder and a unidirectional many-to-one vanilla recurrent neural network operating in tandem.
• We demonstrated that for 64 GBd DP-16-QAM optical transmission over 14× 80 km SSMF, the suggested CRNN-based equalizer outperforms state-of-the-art bidirectional recurrent-based approaches while having >50% lower computational complexity compared to them.
• We also demonstrated that, when phase noise is included, a mere neural network-based nonlinearity mitigation at the end of the linear equalization chain outperforms the joint nonlinearity and polarisation mode dispersion MD mitigation solution after chromatic dispersion compensation in terms of performance and complexity.
We developed a neural network (NN)-based digital pre-distortion (DPD) technique for a high baud rate coherent transmitter that compensates hardware distortions and showed significant improvements compared to other popular nonlinear DPD techniques.
By using this NN-based DPD
• We demonstrated record net data rate of 1.61 Tb/s on single wavelength over 80 km of fiber.
• We achieved a net C-band data rate of 54.5 Tb/s over 48 km of field-deployed fiber.
The 4 PhD students in FONTE submitted 1 patent with Nokia, published 1 book chapter, 6 journal papers, 16 conference papers and several software packages & codes. The consortium organised 22 scientific training events attended by over 1150 external researchers, 4 Transferable Skills Workshops, 5 Project Annual Meetings and completed 25 public engagement activities, reaching all sectors of society.
• Achieved a world record transmission rate of 1.48 Tbps over a typical inter-data-centre link through developing and implementing a deep-neural-network-based de-noiser.
• Improved transmission quality by 5× in a real-world operational 600 km link by developing and implementing a tailored complex-value-based neural network.
• Increased the capacity by 4% at no additional cost in a 2400 km link by learning the optimal link configuration via an autoencoder-based machine learning algorithm.
• Trimmed by 2× training complexity of time-series filtering algorithms and by 6× the required dataset size via developing a data augmentation procedure.
• We developed a novel approach to incorporating loss into the nonlinear Fourier transform based systems exploiting the dispersion decreasing fibres. The proposed solution substantially suppresses the degrading effects of fibre loss.
Together with industrial partner Nokia Bell Labs we proposed and developed a new receiver based on optoelectronic machine learning for intensity-modulated and direct detection systems. The optical pre-processing stage slices the received signal spectrum in small sub-bands with passive optical filters and each is detected by a photodetector with a digital post-processing based on reservoir computing. The method led to a patent filed by the Nokia Bell Labs.
• We proposed and numerically demonstrated the potential of the receiver for 32-GBd OOK signal transmission, showing an increase in reach from 10 km to 40 km, corresponding to 400%, compared with digital-only techniques.
• We validated these findings in an experimental setting at the industry partner facilities. The proposed receiver together with ML-based equalizers enabled to increase the transmission reach from 10 km to more than 80 km, i.e. a more than 800% increase in reach.
To alleviate nonlinear channel impairments in long-haul fibre-optic communications, we developed a neural network equalizer consisting of a convolutional neural network encoder and a unidirectional many-to-one vanilla recurrent neural network operating in tandem.
• We demonstrated that for 64 GBd DP-16-QAM optical transmission over 14× 80 km SSMF, the suggested CRNN-based equalizer outperforms state-of-the-art bidirectional recurrent-based approaches while having >50% lower computational complexity compared to them.
• We also demonstrated that, when phase noise is included, a mere neural network-based nonlinearity mitigation at the end of the linear equalization chain outperforms the joint nonlinearity and polarisation mode dispersion MD mitigation solution after chromatic dispersion compensation in terms of performance and complexity.
We developed a neural network (NN)-based digital pre-distortion (DPD) technique for a high baud rate coherent transmitter that compensates hardware distortions and showed significant improvements compared to other popular nonlinear DPD techniques.
By using this NN-based DPD
• We demonstrated record net data rate of 1.61 Tb/s on single wavelength over 80 km of fiber.
• We achieved a net C-band data rate of 54.5 Tb/s over 48 km of field-deployed fiber.
The 4 PhD students in FONTE submitted 1 patent with Nokia, published 1 book chapter, 6 journal papers, 16 conference papers and several software packages & codes. The consortium organised 22 scientific training events attended by over 1150 external researchers, 4 Transferable Skills Workshops, 5 Project Annual Meetings and completed 25 public engagement activities, reaching all sectors of society.
FONTE has developed several new data-driven approaches to nonlinearity mitigation. The new nonlinear Fourier transform based and neural network based equalisers and signal processing techniques developed within FONTE demonstrated a high quality of signal recovery in the nonlinear fibre channel. ESRs worked closely with the industrial partner Nokia Bell Labs and this allowed them to develop new solutions at the front edge of the modern technology. The important property of the methods developed is that they are adaptive, so can be potentially implemented in real metro links resulting in an improved quality of transmission.
COVID-19 has clearly shown how important high quality communication systems are for the society and for the economy. High quality, fast and reliable transmission fibre networks are the backbone of the global communication infrastructure, which enables dynamic working as well as on-line / on-demand services. Today most business and public activities have essentially become dependent on the speed and quality of virtual information exchange. Research in FONTE has made a meaningful contribution to successfully improve this vital optical communication technology beyond its current limits.
COVID-19 has clearly shown how important high quality communication systems are for the society and for the economy. High quality, fast and reliable transmission fibre networks are the backbone of the global communication infrastructure, which enables dynamic working as well as on-line / on-demand services. Today most business and public activities have essentially become dependent on the speed and quality of virtual information exchange. Research in FONTE has made a meaningful contribution to successfully improve this vital optical communication technology beyond its current limits.