[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83199-en":3,"doc-seo-83199-105":30,"detail-sidebar-cat-0-en-105":83},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83199,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Small Language Model Based Control for BBR over Low Earth Orbit Satellite Internet","Low Earth Orbit (LEO) satellite Internet introduces rapid path variability, intermittent capacity shifts, and nonterrestrial delay dynamics that stress transport-layer congestion control. Although Bottleneck Bandwidth and Roundtrip propagation time (BBR) can deliver high throughput, its aggressive bandwidth probing may trigger excessive retransmissions and unstable pacing on LEO links. The study evaluates BBR on a SpaceX Starlink testbed across distributed AWS endpoints and compares it with Cubic, Vegas, and Hybla under isolated and competing traffic.","Small Language Model-based Control for BBR over Low Earth Orbit Satellite Internet  \nRakshitha De Silva , Shiva Raj Pokhrel  Senior Member, IEEE and Jonathan Kua  Member, IEEE  \narXiv :2607 .07 142v 1 [ cs .NI] 8 Jul 2026  \nAbstract—Low Earth Orbit (LEO) satellite Internet introduces rapid path variability, intermittent capacity shifts, and nonterrestrial delay dynamics that challenge transport-layer congestion control. Although Bottleneck Bandwidth and Roundtrip propagation time (BBR) achieves high throughput in such environments, its aggressive bandwidth probing can cause excessive retransmissions and unstable pacing over LEO links. This paper presents a global experimental evaluation of BBRover a SpaceX Starlink testbed spanning six geographically distributed AWS endpoints and compares its behaviour against Cubic, Vegas, and Hybla under isolated and competing traffic scenarios. The measurements show that BBR consistently delivers superior throughput but incurs significantly higher retransmission overhead, revealing a critical throughput–stability tradeoff in LEO satellite Internet. To address this limitation, we propose a Small Language Model (SLM)-guided BBR adaptation framework that learns phase-safe pacing-gain decisions from real Starlink traces. The framework combines a structured BBR state encoder, LoRA-based parameter-efficient fine-tuning, and a constrained networking head to generate feasible pacing actions with low inference latency. Evaluation using GPT-2, T5, GPT-Neo, and SmolLM2 shows that lightweight SLMs can retain BBR’s throughput advantage while substantially reducing retransmissions, with performance comparable to larger language models but at much lower computational cost.  \nIndex Terms—Bottleneck Bandwidth and Round-trip propagation time (BBR), Network measurements, Starlink Internet, Small Language Model (SLM)  \nI. INTRODUCTION  \nLow Earth Orbit (LEO) satellite mega-constellations [1] have emerged as a cornerstone of next-generation global communications, enabling wide-area broadband with significantly lower latency than traditional satellite systems. SpaceX Starlink represents the largest and most mature constellation to date, alongside efforts such as Eutelsat OneWeb and Amazon Kuiper. Google’s Bottleneck Bandwidth and Roundtrip propagation time (BBR) Congestion Control Algorithm (CCA) [2] marks a paradigm shift in Transmission Control Protocol (TCP) congestion control by explicitly modeling bottleneck bandwidth and propagation delay rather than relying on loss-based detection. Its latest iteration, BBRv3 [2] 1 , refines probing and pacing to optimize throughput, latency, and fairness across diverse conditions. In parallel, Small  \nThis work is supported by SmartSat CRC, whose activities are funded by the Australian Government’s CRC Program.  \nR. De Silva, S. R. Pokhrel and J. Kua are with the IoT & Software Engineering Research Lab, School of Information Technology, Deakin University, Geelong, VIC 3125, Australia (e-mail: [rakshitha.desilva@deakin.edu.au](rakshitha.desilva@deakin.edu.au); [shiva.pokhrel@deakin.edu.au](shiva.pokhrel@deakin.edu.au); [jonathan.kua@deakin.edu.au](jonathan.kua@deakin.edu.au)).  \n1For the rest of the paper, if not stated otherwise, Bottleneck Bandwidth and Round-trip propagation time (BBR)v3 is referred to as BBR.  \nLanguage Models (SLMs), a subclass of Large Language Models (LLMs) designed to operate under strict compute, memory, and latency constraints [3], retain meaningful language understanding within tight hardware budgets, making them well suited to edge, mobile, and embedded settings [4] and, in particular, to network-driven tasks that demand realtime inference close to the data source [5] .  \nThe recent surge in mega constellations has prompted industry and academia to optimize data transmission over satellite networks, generalizing it as a replacement and extension of well-established terrestrial networks [6] . Internet congestion control is a well-explored probl","cbCail0vw0ulOdJO","https://ap.wps.com/l/cbCail0vw0ulOdJO","pdf",4022507,2,1,10,"English","en",105,"# Introduction\n## LEO satellite Internet challenges\n## BBR congestion control and motivation\n## Prior congestion control work and limitations\n## Learning-based congestion control approaches","[{\"question\":\"How does the proposed Small Language Model (SLM) improve BBR behavior?\",\"answer\":\"The framework uses an SLM to learn phase-safe pacing-gain decisions from real Starlink traces, combining a structured BBR state encoder, LoRA-based fine-tuning, and a constrained networking head to generate feasible pacing actions with low inference latency.\"}]",1784185905,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"small-language-model-based-control-for-bbr-over-low-earth-orbit-satellite-internet","",{"@graph":36,"@context":77},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/small-language-model-based-control-for-bbr-over-low-earth-orbit-satellite-internet/83199/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How does the proposed Small Language Model (SLM) improve BBR behavior?","Question",{"text":75,"@type":76},"The framework uses an SLM to learn phase-safe pacing-gain decisions from real Starlink traces, combining a structured BBR state encoder, LoRA-based fine-tuning, and a constrained networking head to generate feasible pacing actions with low inference latency.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":22,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]