[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85498-en":3,"doc-seo-85498-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85498,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A Benchmarking Framework for PON-Based Fronthaul Network Design","A unified benchmarking framework standardizes cost catalogs and deployment scenarios for PON-based fronthaul network design, enabling objective comparison of optimization strategies. The study formulates the design problem as Integer Linear Programming (ILP) to derive optimality bounds and evaluates three scalable heuristics: a Genetic Algorithm, K-Means Clustering (KMC+), and a graph-based Randomized Successive Splitter Assignment (RSSA+). Simulations show that time-limited ILP remains a strong reference, while RSSA+ achieves near-ILP performance with guaranteed feasibility across evaluated scenarios.","arXiv :2601 . 14480v2 [ cs .NI] 13 Jul 2026  \n\n| Research Article | 1 |\n| --- | --- |\n\nA benchmarking framework for PON-based fronthaul network design  \nEGEMEN ERBAYAT1,* , GUSTAVO B. FIGUEIREDO2 , SHIH-CHUN LIN3 , MOTOHARU MATSUURA4 , HIROSHI HASEGAWA5 , AND SURESH SUBRAMANIAM1  \n1 The George Washington University, USA, 2 Federal University of Bahia, Brazil, 3 North Carolina State University, USA, 4 University of Electro-Communications, Japan, 5 Nagoya University, Japan  \n*Corresponding author: [erbayat@gwu.edu](erbayat@gwu.edu)  \nCompiled July 14, 2026  \nAs 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. © 2026 Optical Society of America  \n[http://dx.doi.org/10.1364/ao.XX.XXXXXX](http://dx.doi.org/10.1364/ao.XX.XXXXXX)  \n1. INTRODUCTION  \nThe unprecedented demand for ultra-high-speed, low-latency, and reliable connectivity is reshaping mobile network architecture as the industry transitions from 5G to the vision of 6G [1, 2] . Driven by applications such as immersive extended reality (XR), autonomous mobility, massive Internet of Things (IoT), and the tactile Internet, future wireless systems must support orders-of-magnitude growth in data volume, device density, and service diversity [3] . To meet these performance goals, Radio Access Networks (RANs) are becoming increasingly disaggregated, with baseband functions centralized and cell deployments densified. While Centralized or Cloud-RAN (C-RAN) architectures provide enhanced spectral efficiency and lower operational cost, they place heavy demands on the fronthaul segment linking remote radio heads (RRHs) with centralized baseband units (BBUs) [4–6] . As fronthaul links are required to simultaneously provide multi-gigabit throughput, sub-millisecond latency, and synchronization precision, transport acts as a critical bottleneck for scalable 5G and 6G deployments [7–9] .  \nPassive Optical Networks (PONs) provide a high-capacity, low-latency, and cost-effective solution for delivering fiber-based transport from the central office to numerous cell towers [10] .  \nSince PONs facilitate shared fiber infrastructure and avoid the need for a dedicated fiber per tower, they are uniquely suitable for scenarios where multiple cell sites need to be served economically [11] . Modern standards such as 10-Gigabit-capable Symmetric PON (XGS-PON), Next-Generation PON 2 (NG-PON2), and 50-Gigabit-capable PON (50G-PON) offer bandwidths up to 10–50 Gbps per wavelength and support time and/or wavelength division multiplexing [12–15] . Consequently, they are well-suited to carry fronthaul traffic for low-layer functional splits such as 3rd Generation Partnership Project (3GPP) Option 7.2 [16] .  \nA wide range of research studies have examined the utilization of PONs for fronthaul within 4G, 5G, and anticipated 6G networks. W","cbCaiv6qDgWcXrjF","https://ap.wps.com/l/cbCaiv6qDgWcXrjF","pdf",3131742,1,16,"English","en",105,"# Introduction\n## Background: 5G/6G RAN and fronthaul constraints\n## PONs for fronthaul transport\n## Need for standardized evaluation\n# Proposed benchmarking framework\n## Standardized cost catalog and deployment scenarios\n## ILP optimality bounds and heuristic evaluation","[{\"question\":\"Why is a standardized benchmarking framework needed for PON-based fronthaul design?\",\"answer\":\"Current literature uses different assumptions and non-standard evaluation models, which makes performance claims context-dependent and not directly comparable. The paper addresses this by standardizing cost catalogs and deployment scenarios.\"},{\"question\":\"How does the paper model the fronthaul network design problem?\",\"answer\":\"The design problem is formulated as an Integer Linear Programming (ILP) task to establish optimality bounds. These bounds are used as a reference when assessing heuristic strategies.\"},{\"question\":\"Which scalable heuristic algorithms are evaluated, and what do the results show?\",\"answer\":\"The paper evaluates a Genetic Algorithm, K-Means Clustering (KMC+), and a graph-based Randomized Successive Splitter Assignment (RSSA+). Simulation results indicate RSSA+ consistently attains near-ILP performance while maintaining feasibility across evaluated scenarios.\"}]",1784204027,40,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"a-benchmarking-framework-for-pon-based-fronthaul-network-design","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/a-benchmarking-framework-for-pon-based-fronthaul-network-design/85498/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is a standardized benchmarking framework needed for PON-based fronthaul design?","Question",{"text":75,"@type":76},"Current literature uses different assumptions and non-standard evaluation models, which makes performance claims context-dependent and not directly comparable. The paper addresses this by standardizing cost catalogs and deployment scenarios.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper model the fronthaul network design problem?",{"text":80,"@type":76},"The design problem is formulated as an Integer Linear Programming (ILP) task to establish optimality bounds. These bounds are used as a reference when assessing heuristic strategies.",{"name":82,"@type":73,"acceptedAnswer":83},"Which scalable heuristic algorithms are evaluated, and what do the results show?",{"text":84,"@type":76},"The paper evaluates a Genetic Algorithm, K-Means Clustering (KMC+), and a graph-based Randomized Successive Splitter Assignment (RSSA+). Simulation results indicate RSSA+ consistently attains near-ILP performance while maintaining feasibility across evaluated scenarios.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":28,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]