A benchmarking framework for PON-based fronthaul network design
For researchers and engineers designing PON-based fronthaul networks, this work provides a standardized evaluation framework and demonstrates that constraint-aware algorithms like RSSA+ are superior to simpler heuristics.
This paper proposes a unified benchmarking framework for PON-based fronthaul network design, using ILP to establish optimality bounds and evaluating three heuristics. RSSA+ achieves near-ILP performance, outperforming simpler heuristics.
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.