Small Language Model-based Control for BBR over Low Earth Orbit Satellite Internet

Abstract: 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 LLM (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 LLMs but at much lower computational cost.