Channel Ordering for Fairness in Elastic Optical Networks via a LLM-Guided Bottleneck TSP Solver

Abstract

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.