Secrecy-Driven Robust Beamforming Under Age of Information Constraints for Satellite-Terrestrial Integrated Networks
Bibliographic record
Abstract
This paper presents a robust secure beamforming (BF) strategy that incorporates information freshness into the physical layer security (PLS) design of satellite-terrestrial integrated network (STIN). In the considered framework, satellite networks serve earth stations in the presence of multiple eavesdroppers, while terrestrial networks, operating over the same spectrum, provide multicast services to ground users. To capture the dynamics of status update timeliness, we model the evolution of the Age of Information (AoI) using a discrete-time Markov chain, taking into account both the activation thresholds and access probabilities of users. Based on this model, we derive a closed-form expression for the secrecy margin of the wiretap channel, thereby jointly quantifying data freshness and communication security. Assuming imperfect channel state information, we formulate an optimization problem to maximize the average secrecy margin, subject to constraints on average AoI, quality of service (QoS), eavesdropping probability, and total transmit power. To tackle the inherent non-convexity caused by probabilistic constraints, we adopt Bernstein-type inequalities to transform them into tractable deterministic equivalents. An efficient solution algorithm is then developed by integrating successive convex approximation with difference-of-convex programming, enabling effective computation of the optimal BF vectors. Extensive simulation results confirm that the proposed approach achieves notable improvements over existing methods in both secrecy performance and information freshness, offering a unified and practical solution for secure and timely communication in future 6 G integrated networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".