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Creators/Authors contains: "Kilper, Dan"

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  1. ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available May 6, 2026
  2. ; ; ; ; ; ; ; (, Optica Publishing Group)
    We implement a cascaded learning framework leveraging three different EDFA and fiber component models for OSNR and GSNR prediction, achieving MAEs of 0.20 and 0.14 dB over a 5-span network under dynamic channel loading. 
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    Free, publicly-accessible full text available March 30, 2026
  3. ; ; ; ; ; ; ; ; (, Optica Publishing Group)
    Optical transmission systems require accurate modeling and performance estimation for autonomous adaption and reconfiguration. We present efficient and scalable machine learning (ML) methods for modeling optical networks at component- and network-level with minimized data collection. 
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    Free, publicly-accessible full text available March 30, 2026
  4. ; ; (, IEEE)
    Full Text Available 
  5. ; ; ; ; ; (, 50th European Conference on Optical Communication)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; (, 50th European Conference on Optical Communication)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; (, Optica Publishing Group)
    We implement a cascaded learning framework using component-level EDFA models for optical power spectrum prediction in multi-span networks, achieving a mean absolute error of 0.17 dB across 6 spans and 12 EDFAs with only one-shot measurement. 
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    Full Text Available 
  8. ; ; ; ; ; (, IEEE)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; ; ; ; (, Journal of Lightwave Technology)
    Full Text Available 
  10. ; ; (, IEEE/Optica Optical Fiber Communication Conference (OFC’23))
    We implement and test transfer learning-based gain models across 16 ROADM EDFAs, which achieve less than 0.17/0.30 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target EDFA dataset. 
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    Full Text Available