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A benchmarking framework for PON-based fronthaul network design

Egemen Erbayat, Gustavo B. Figueiredo, Shih-Chun Lin, Motoharu Matsuura, Hiroshi Hasegawa, Suresh Subramaniam

TL;DR

This work addresses the lack of standardized evaluation models for PON-based fronthaul design in 5G/6G by proposing a unified benchmarking framework with a fixed cost catalog and deployment scenarios. It formulates the PON-FD problem as an exact ILP to establish optimality bounds and evaluates four approaches—time-limited ILP, GA, KMC+, and RSSA+—within the same framework. Results show time-limited ILP provides strong baselines that often exceed heuristic performance, while RSSA+ achieves near-ILP costs and robust feasibility across scenarios; KMC+ performs well in dense RU deployments but can falter under tighter constraints, and GA generally underperforms due to constraint handling. The framework enables reproducible, interpretable comparisons and delivers practical insights into DU and splitter placement, infrastructure sharing, and latency trade-offs, with complete datasets publicly available for community use.

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].

A benchmarking framework for PON-based fronthaul network design

TL;DR

This work addresses the lack of standardized evaluation models for PON-based fronthaul design in 5G/6G by proposing a unified benchmarking framework with a fixed cost catalog and deployment scenarios. It formulates the PON-FD problem as an exact ILP to establish optimality bounds and evaluates four approaches—time-limited ILP, GA, KMC+, and RSSA+—within the same framework. Results show time-limited ILP provides strong baselines that often exceed heuristic performance, while RSSA+ achieves near-ILP costs and robust feasibility across scenarios; KMC+ performs well in dense RU deployments but can falter under tighter constraints, and GA generally underperforms due to constraint handling. The framework enables reproducible, interpretable comparisons and delivers practical insights into DU and splitter placement, infrastructure sharing, and latency trade-offs, with complete datasets publicly available for community use.

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].
Paper Structure (24 sections, 21 equations, 2 figures, 2 tables, 4 algorithms)

This paper contains 24 sections, 21 equations, 2 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Modern RAN Architectures and Fronthaul Requirements
  4. Fronthaul Transport Technologies and PON Architectures
  5. Related Work
  6. Benchmarking Framework Design
  7. Common Assumptions
  8. Network Services
  9. Optimization Methods Under Study
  10. Problem Formulation
  11. Objective Function.
  12. Alternative Solution Methods
  13. Evaluation Methodology
  14. Numerical Results
  15. Conclusion
  16. ...and 9 more sections

Figures (2)

  • Figure 1: Proposed benchmarking architecture with one DU.
  • Figure 2: Fronthaul performance across deployment scenarios.