Covert Communication in Large-Scale Multi-Tier LEO Satellite Networks
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Bibliographic record
Abstract
We leverage covert communication to enhance the security of a large-scale multi-tier Low Earth Orbit (LEO) satellite network against vigilant adversarial terrestrial Base Stations (BSs) aiming at detecting satellite transmissions. This approach involves deploying massive LEO satellites at different altitudes around Earth to form a multi-tier network serving as a backhaul for near-ground Unmanned Aerial Vehicles (UAVs) that provide network services to terrestrial mobile users. Meanwhile, terrestrial BSs attempt to detect satellite transmissions based on their own received signal powers. To evade detection, the LEO satellite network performs power control to obscure the satellite transmission within the co-channel interference among the LEO satellites. We formulate a two-stage Stackelberg game to model the conflict dynamics between the terrestrial BSs and the LEO satellite network. In this game, the terrestrial BSs act as non-cooperative followers at the lower stage aiming to minimize their detection errors. On the other hand, the LEO satellite network acts as the leader at the upper stage aiming to maximize its utility while ensuring communication covertness. In contrast to existing works that focus on a small set of network nodes, our study considers a large-scale multi-tier LEO satellite network and employs stochastic geometry to model the spatial distribution of network nodes. To achieve the Stackelberg equilibrium, we develop a bi-level algorithm based on Successive Convex Approximation (SCA) and golden-section search. Our numerical results provide practical insights, revealing a trade-off in leveraging co-channel interference (i.e., while it improves the communication covertness of satellite transmission, it simultaneously degrades the link reliability).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it