A benchmarking framework for PON-based fronthaul network design

2026-01-20

AI Summary

This work addresses the lack of a unified evaluation benchmark for fronthaul network optimization, which hinders objective comparison among different strategies. To this end, the authors establish a standardized benchmark framework featuring a consistent cost model and deployment scenarios, and introduce integer linear programming (ILP) as the first optimal reference baseline. Building upon this foundation, they propose RSSA+, a constraint-aware, graph-theoretic algorithm that effectively balances solution quality and scalability. Experimental results demonstrate that RSSA+ consistently achieves near-ILP performance across all tested scenarios while guaranteeing feasibility, significantly outperforming conventional heuristic approaches. The complete framework, algorithm implementation, and datasets have been made publicly available.

Technology Category

• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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].

Problem

• PON
• fronthaul
• benchmarking
• network design
• 5G/6G RAN

Innovation

• benchmarking framework
• Passive Optical Network (PON)
• Integer Linear Programming (ILP)
• fronthaul network design
• RSSA+