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MACHINE LEARNING TECHNIQUES FOR SELECTING PATHS IN MULTI-VENDOR RECONFIGURABLE OPTICAL ADD/DROP MULTIPLEXER NETWORKS
MACHINE LEARNING TECHNIQUES FOR SELECTING PATHS IN MULTI-VENDOR RECONFIGURABLE OPTICAL ADD/DROP MULTIPLEXER NETWORKS
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机译:在多厂商可重构光学增/减复用器网络中选择路径的机器学习技术
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摘要
Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.
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