A benchmarking framework for PON-based fronthaul network design

TL;DR

This paper introduces a standardized benchmarking framework for PON-based fronthaul network design, enabling objective comparison of optimization strategies and providing insights into the effectiveness of ILP and heuristic algorithms.

Contribution

It proposes a unified evaluation model, formulates the design problem with ILP, and assesses scalable heuristics, highlighting the robustness of ILP and the efficiency of the RSSA+ algorithm.

Findings

ILP provides strong optimality bounds and outperforms heuristics.

RSSA+ achieves near-ILP performance with guaranteed feasibility.

The benchmarking framework and datasets are publicly available.

Abstract

As mobile networks transition toward 5G and 6G RAN architectures, Passive Optical Networks (PONs) offer a critical solution for cost-effective fronthaul transport. However, the lack of standardized evaluation models in current literature makes an objective comparison of diverse optimization strategies difficult. This paper addresses this gap by proposing a unified benchmarking framework that standardizes cost catalogs and deployment scenarios. We formulate the network design problem using Integer Linear Programming (ILP) to establish optimality bounds and evaluate three scalable heuristic strategies: a Genetic Algorithm, K-Means Clustering (KMC+), and a graph-based Randomized Successive Splitter Assignment (RSSA+) algorithm. Simulation results show that a time-limited ILP remains a strong reference point, even when optimality is not reached. Despite being rarely used in prior fronthaul planning studies, it consistently yields solutions superior to those produced by standard heuristic methods. Among scalable approaches, RSSA+ reliably attains near-ILP performance while ensuring feasibility across all evaluated scenarios, which underscores the importance of advanced, constraint-aware algorithmic designs over simpler heuristics. The complete benchmarking framework and datasets are publicly shared in [1].