Amalgamation of Machine Learning Techniques with Optical Systems: A Futuristic Approach
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48781-1_1
Reference22 articles.
1. Wang, D., et al.: Intelligent constellation diagram analyzer using convolutional neural network-based deep learning. Opt. Express 25(15), 17150–17166 (2017)
2. Tizikara, D.K., et al.: Machine learning-aided optical performance monitoring techniques: a review. Front. Comms. Net. 2, 756513 (2022). https://doi.org/10.3389/frcmn.2021.756513
3. Wang, D., Zhang, M.: Artificial intelligence in optical communications: from machine learning to deep learning. Front. Commun. Net. 2, 656786 (2021). https://doi.org/10.3389/frcmn.2021.656786
4. Wang, D., et al.: Comprehensive eye diagram analysis: a transfer learning approach. IEEE Photonics J. 11(6), 1–19 (2019)
5. Esteves, S., Rebola, J., Santana, P.: Deep learning for BER prediction in optical connections impaired by inter-core crosstalk. In: 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), pp. 440–445. Porto, Portugal (2022)
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