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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.11971 |
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| _version_ | 1866909903770091520 |
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| author | Wu, Liangshun Chen, Wen Wu, Qingqing |
| author_facet | Wu, Liangshun Chen, Wen Wu, Qingqing |
| contents | In flexible-grid elastic optical networks (EONs), the ordering of frequency channels plays a crucial role in managing inter-channel interference and ensuring signal quality. We address the Channel Ordering Problem (COP) by reformulating it as a Bottleneck Traveling Salesman Problem (BTSP), where interference among channels is represented as edge weights in a graph structure. To tackle this challenge efficiently, we develop a scalable approach that integrates statistical exploration with guidance from large language models (LLMs). Extensive simulations using both the Gaussian Noise (GN) model and the split-step Fourier method demonstrate that our method achieves near-optimal signal-to-noise ratio (SNR) performance and offers robust scalability across diverse network settings, making it well-suited for practical deployment in large-scale optical communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11971 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel Ordering for Fairness in Elastic Optical Networks via a LLM-Guided Bottleneck TSP Solver Wu, Liangshun Chen, Wen Wu, Qingqing Optics In flexible-grid elastic optical networks (EONs), the ordering of frequency channels plays a crucial role in managing inter-channel interference and ensuring signal quality. We address the Channel Ordering Problem (COP) by reformulating it as a Bottleneck Traveling Salesman Problem (BTSP), where interference among channels is represented as edge weights in a graph structure. To tackle this challenge efficiently, we develop a scalable approach that integrates statistical exploration with guidance from large language models (LLMs). Extensive simulations using both the Gaussian Noise (GN) model and the split-step Fourier method demonstrate that our method achieves near-optimal signal-to-noise ratio (SNR) performance and offers robust scalability across diverse network settings, making it well-suited for practical deployment in large-scale optical communication systems. |
| title | Channel Ordering for Fairness in Elastic Optical Networks via a LLM-Guided Bottleneck TSP Solver |
| topic | Optics |
| url | https://arxiv.org/abs/2511.11971 |