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

TL;DR

This paper introduces a novel LLM-guided approach to solve the Channel Ordering Problem in elastic optical networks by reformulating it as a Bottleneck TSP, achieving near-optimal performance and scalability.

Contribution

It presents a scalable, LLM-guided method for solving the COP by reformulating it as a BTSP, combining statistical exploration with language model guidance.

Findings

Achieves near-optimal SNR performance in simulations.

Demonstrates robust scalability across diverse network scenarios.

Effective in practical large-scale optical systems.

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.