Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866910888458452992 |
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| author | Raj, Agastya Wang, Zehao Slyne, Frank Chen, Tingjun Kilper, Dan Ruffini, Marco |
| author_facet | Raj, Agastya Wang, Zehao Slyne, Frank Chen, Tingjun Kilper, Dan Ruffini, Marco |
| contents | We introduce a ML-based architecture for network operators to detect impairments from specific OSaaS users while blind to the users' internal spectrum details. Experimental studies with three OSaaS users demonstrate the model's capability to accurately classify the source of impairments, achieving classification accuracy of 94.2%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks Raj, Agastya Wang, Zehao Slyne, Frank Chen, Tingjun Kilper, Dan Ruffini, Marco Networking and Internet Architecture We introduce a ML-based architecture for network operators to detect impairments from specific OSaaS users while blind to the users' internal spectrum details. Experimental studies with three OSaaS users demonstrate the model's capability to accurately classify the source of impairments, achieving classification accuracy of 94.2%. |
| title | Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.17079 |