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].

翻译：随着移动网络向5G和6G无线接入网架构演进，无源光网络为经济高效的前传传输提供了关键解决方案。然而，当前文献中标准化评估模型的缺乏使得对不同优化策略进行客观比较变得困难。本文通过提出一个统一基准测试框架来弥补这一不足，该框架标准化了成本目录与部署场景。我们采用整数线性规划对网络设计问题进行建模以确立最优性边界，并评估了三种可扩展的启发式策略：遗传算法、K-Means聚类算法以及基于图的随机连续分路器分配算法。仿真结果表明，即使在未达到最优解的情况下，时间受限的整数线性规划仍能提供有力的参考基准。尽管在先前的传规划研究中鲜少使用，该方法始终能产生优于标准启发式算法的解决方案。在可扩展算法中，随机连续分路器分配算法能稳定实现接近整数线性规划的性能，同时确保在所有评估场景中的可行性，这凸显了先进且具备约束感知能力的算法设计相对于简单启发式方法的重要性。完整的基准测试框架与数据集已在[1]中公开共享。