Low Earth Orbit (LEO) satellite mega-constellations such as Starlink require frequent handovers due to high orbital velocities, resulting in substantial Round-Trip Time (RTT) fluctuations. Accurate RTT prediction can mitigate these fluctuations, yet it fundamentally relies on continuously updated serving- satellite information. Existing serving-satellite identification methods typically complete identification only at the end of each scheduling cycle, creating an identification blind zone that limits real-time prediction. To overcome this limitation, we develop StarRTT, an end-to-end real-time framework that eliminates the blind zone by integrating per-second serving-satellite identification with RTT prediction. Within this framework, we propose Real-Time Long Short-Term Memory (RT-LSTM), an LSTM-based RTT prediction model designed to capture both non-stationary RTT evolution and time-varying geometric relationships among the serving satellite, user terminal, and gateways. Specifically, a historical LSTM encodes historical RTT observations and real-time serving-satellite and gateway-relative features, while a future LSTM processes Two-Line Element (TLE)-propagated future features of the identified serving satellite up to the next scheduling instant. Under strict real-time back-testing, RT-LSTM achieves a Mean Absolute Error (MAE) of 1.405 ms averaged over all 15 prediction horizons and 15 reference time-slot indices, reducing the error by 4.3 %, 11.6 %, and 22.1 % compared with the existing baseline models, LSTM, StarNet, and Attention-based Spatiotemporal Model (ASTM), respectively. Notably, the reduction over LSTM reaches 12.5 % at the scheduling instant, highlighting the benefit of real-time serving-satellite information when recent RTT observations are limited.
Figures & tables
Fig. 1: Serving-satellite identification using per-second XOR operations on consecutive obstruction maps. The observed position Xt is converted into (θt,ϕt) , and the observed trajectory is compared with TLE-based candidate trajectories to identify the serving satellite.
Fig. 2: Timeline comparison between conventional scheduling-cycle-based serving-satellite identification and the proposed real-time framework. Scheduling-cycle-based methods incur a blind zone lasting 14 seconds, whereas the proposed method enables reliable identification for real-time prediction.
Fig. 3: System architecture of the StarRTT framework, illustrating real-time serving-satellite identification and the proposed RT-LSTM with dual LSTM branches for encoding historical RTT observations, serving-satellite, and gateway-relative features, and TLE-propagated future features, followed by gated fusion for RTT prediction.
Parameter
Value
Collection Period (UTC)
2026-03-18 05:53 - 2026-03-23 00:20
UT Latitude
37.5825615 °
UT Longitude
127.0262699 °
UT Altitude
90.0m
Model / Subscription
STANDARD 4X (Gen 3) / Residential
Network Load
iPerf3 TCP downlink w/o bandwidth limitation
TABLE I: Experimental Environment.
Model \ Timeline
Past windows
Current window before t
Reference time
Bounded future
[tstart=t−44,b(t)−16]
[b(t)−15,t−1]
[t]
[t+1,b(t)−1]
LSTM [ 6 ]
RTT, Satellite data
RTT
RTT
×
ASTM [ 5 ]
RTT, Satellite data, DGCN
RTT
RTT
×
StarNet [ 4 ]
RTT, Satellite data, tstart=t−29
RTT
RTT
×
RT-LSTM (Proposed)
RTT, Gateway-relative, Satellite data
RTT, Gateway-relative, Satellite data
RTT, Gateway-relative, Satellite data
Gateway-relative, Satellite data
TABLE II: Comparison of Model Input Timelines.
Fig. 4: Empirical analysis of Starlink link mode and RTT across satellite generations (V1.0, V1.5, V2.0-mini). (a) Architectural comparison of bent-pipe and ISL modes. (b) Spatial distribution of average RTT and connected time, highlighting the bent-pipe coverage area formed by the geometric intersection of the UT’s FOV and bent-pipe gateways.
Fig. 5: Comparison of RTT prediction performance under strict real-time back-testing. (a) Comparison of the MAE, averaged over the 15 -step prediction horizon, for all evaluated models at each reference time-slot index within a 15 -second scheduling cycle. (b) Per-step MAE comparison at reference time-slot index 12 (the scheduling instant) over the 15 -step prediction horizon, divided into short-term, mid-term, and long-term intervals.
Low-Earth orbit (LEO) satellite Internet has become an indispensable infrastructure that provide growing coverage for global users. Despite extensive measurement efforts, the principles underlying region-level performance characteristics remain insufficiently understood, limiting the ability to identify region-specific latency signatures under dynamic network conditions. In this paper, we formulate the problem of region-level latency characterization using Starlink round-trip time (RTT) measurements from the public LENS dataset. We then propose a hierarchical analytical framework that transforms raw RTT sequences into multi-scale statistical features for cross-region comparison. Using data from five geographically representative regions, we demonstrate that latency differences are strongly associated with deployment factors, particularly infrastructure availability and Starlink dish-to-Point-of-Presence distance. Mutual information analysis identifies minimum RTT as the most discriminative feature, which is further supported by XGBoost-based feature importance. The proposed model well achieves 83% accuracy on short-term data. However, its performance degrades over longer periods, indicating limited temporal generalization and motivating the need for adaptive models and feature representations for long-term performance in the future.
Xiang Shi, Yifei Zhang, Peng Hu
Advanced Network and Embedded Systems Lab (AEL), Dept. of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada
Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale. To address this, we propose FedRings, a decentralized framework that organizes satellites into ring-based communication structures. It uses a spatio-temporal routing strategy with link-aware communication scheduling to align model exchange with actual visibility windows and time-varying connectivity patterns in LEO. Model updates are propagated along the ring using adaptive sparse incremental aggregation, which reduces communication overhead by progressively combining and compressing updates. To handle communication interruptions, a historical compensation mechanism maintains training continuity. By combining topology-aware routing, communication scheduling, and efficient aggregation, FedRings enables stable and efficient learning in dynamic LEO networks while reducing communication cost, and experiments show it consistently outperforms existing methods in realistic settings.
Ziwu Liu, Inês Pinto Gouveia, Rehana Yasmin +2
CEMSE Division, King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900, Kingdom of Saudi Arabia
LEO satellite networks feature dynamic topologies, time-varying links, and diverse service requirements, which make conventional routing schemes difficult to support fine-grained quality-of-service (QoS) provisioning. Existing studies mainly optimize routing over network states with predefined objectives, but rarely address the practical challenge of translating unstructured natural-language service requests into adaptive routing decisions. To bridge this gap, we propose STR-Agent, an LLM-driven framework for QoS-aware routing in LEO satellite networks. The key innovation of STR-Agent lies in unifying intent perception, tool-based execution, experience accumulation, and reflection-based policy adaptation within a single agent architecture. Specifically, the Perception Module converts natural-language requests into structured routing semantics, while the Reflection Module dynamically adjusts the service-to-routing-policy mapping according to real-time congestion conditions and historical routing outcomes, rather than relying on a fixed routing objective. In addition, we develop a specialized perception model, and construct a domain-specific supervised fine-tuning dataset for LEO service understanding. Simulation results in a Walker-Delta constellation show that STR-Agent significantly outperforms conventional baselines: it reduces end-to-end delay by up to 60% compared with DQ-Dijkstra, improves average intent-understanding accuracy from 45.4% to 92.45% after supervised fine-tuning, and the Reflection Module further reduces the delay by 120 ms at 600 Mbps. These results demonstrate the potential of LLM-driven agent architectures to enable service-aware and adaptive QoS routing in future LEO satellite networks.
Bowen Lu, Mugen Peng, Yaohua Sun +3
State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications