Deep intelligent spectral labelling and receiver signal distribution for optical links

A unique automatic receiver signal distribution strategy is proposed for private optical networks based on the concept of non-orthogonality. A non-orthogonal signal waveform can compress the spectral bandwidth, which not only fits a signal in a bandwidth limited scenario, but also enables the compression ratio information for labelling. Depending on a unique value of spectral compression, an end user destination can be correlated. A network edge node will rely on deep learning to intelligently identify each raw signal and forward it to corresponding end users with no sophisticated digital signal pre-processing. In this case, signal identification and distribution are faster while computationally intensive signal compensation and detection will be shifted to each end user since the receiver is highly dynamic and user-defined in private optical networks. An intelligent signal classifier will be trained considering various fiber transmission factors such as transmission distance, training dataset size and launch power. At the end, a universal classifier is obtained, which can be used to identify signals in a system for any fiber transmission distance and launch power. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License.

Automated summary

A unique automatic signal distribution strategy based on the concept of non-orthogonality is proposed for private optical networks. Non-orthogonal signal waveforms compress spectral bandwidth, enabling fast signal identification and distribution in the network.