Rakshitha De Silva

Rakshitha De Silva, Shiva Raj Pokhrel, Jonathan Kua

Low Earth Orbit (LEO) satellite Internet introduces rapid path variability, intermittent capacity shifts, and non-terrestrial delay dynamics that challenge transport-layer congestion control. Although Bottleneck Bandwidth and Round-trip 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 BBR over 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 trade-off 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.

Rakshitha De Silva, Shiva Raj Pokhrel, Jonathan Kua

This experimental study delivers a global assessment of Google's Bottleneck Bandwidth and Round-trip propagation time-version 3 (BBR-v3) Congestion Control Algorithm (CCA) over SpaceX's Starlink network. Leveraging a strategically deployed six-city testbed across five continents, we systematically benchmark BBR-v3 against eight CCAs: Cubic, Hybla, Vegas, LeoCC, Copa, PCC, BBR-v1, and BBR-v2 under both dedicated and concurrent conditions. Our results demonstrate that BBR-v3's advantage is not aggressive bandwidth capture, but a more balanced fairness, loss, and delay trade-off over the Starlink Internet. We develop pragmatic mathematical models that capture Starlink's complex network dynamics and characterize BBR-v3 behavior to better explain the experimental observations. Our extensive evaluation of queue buildup and fairness further demonstrates BBR-v3's capability to maximize throughput in high-latency, variable satellite environments, while maintaining a balance between aggressiveness and fairness. The findings establish BBR-v3 as a compelling CCA for Low Earth Orbit (LEO) satellite networks and provide a principled analytical foundation for next generation satellite Internet transport design.

Rakshitha De Silva, Shiva Raj Pokhrel, Jonathan Kua et al.

Satellite Communication (SatCom) networks represent a fundamental pillar in modern global connectivity, facilitating reliable service and extensive coverage across a plethora of applications. The expanding demand for high-bandwidth services and the proliferation of mega satellite constellations highlight the limitations of traditional exclusive satellite spectrum allocation approaches. Cognitive Radio (CR) leading to Cognitive Satellite (CogSat) networks through Dynamic Spectrum Management (DSM), which enables the dynamic adaptability of radio equipment to environmental conditions for optimal performance, presents a promising solution for the emerging spectrum scarcity. In this survey, we explore the adaptation of intelligent DSM methodologies to SatCom, leveraging satellite network integrations. We discuss contributions and hurdles in regulations and standardizations in realizing intelligent DSM in SatCom, and deep dive into DSM techniques, which enable CogSat networks. Furthermore, we extensively evaluate and categorize state-of-the-art Artificial Intelligence (AI)/Machine Learning (ML) methods leveraged for DSM while exploring operational resilience and robustness of such integrations. In addition, performance evaluation metrics critical for adaptive resource management and system optimization in CogSat networks are thoroughly investigated. This survey also identifies open challenges and outlines future research directions in regulatory frameworks, network architectures, and intelligent spectrum management, paving the way for sustainable and scalable SatCom networks for enhanced global connectivity.