Machine Learning to mitigate optical physical layer impairments
Next generation of optical networks will be more dynamic in WDM bandwidth assignment. Rapid addition and deletion
of channels presents a challenge to the power stability of the network. In a lightpath with cascaded Erbium Doped Fiber
Amplifiers (EDFA), power excursions of each amplifier during channel configurations may accumulate. We introduced a
machine learning technique for optimized assignment of channels that reduces the impact of power divergence in EDFAs.
Power divergence during rapid channel provisioning is characterized from historical operation records of the network.
Using machine intelligence, we can identify the channel dependence of power divergence.
Industrial Collaborations
Lightwave Research Laboratory has a wide-range of
industrial partners, ranging from equipment vendors to
network operators. Valuing the importance of applied
research, we join their R&D activities for their current
and next-generation products. Calient Technologies
and Polatis (optical switch vendors) and AT&T are
examples of our industrial partners in the optical
network research activities.
Dr. Keren Bergman
Research Professor
Dr. Payman Samadi
Research Scientist
Yishen Huang
Ph.D. Candidate
Yiwen Shen
Ph.D. Candidate
www.cian-erc.org
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