Disrupt to Protect: Interference-Aware Security for HAPS-based Wireless Systems
Bibliographic record
Abstract
In this paper, we explore physical layer security in high-altitude platform station (HAPS)-enabled wireless networks. The inherent characteristics of HAPS transmissions, such as extensive coverage and line-of-sight propagation, render legitimate user signals highly susceptible to passive eavesdropping. To address this security challenge, we consider the deployment of friendly jammers within a designated secrecy-vulnerable region (SVR) to impair the reception capabilities of potential eavesdroppers. To control the resulting interference at eavesdroppers and legitimate users, we propose a region-based jammer deactivation (RJD) scheme, whereby all jammers located within a specified protection region surrounding each user are deactivated. Leveraging tools from stochastic geometry, we derive analytical expressions for coverage and secrecy performances, thereby enabling a comprehensive system-level analysis of the RJD scheme under spatially random network configurations. Numerical evaluations, corroborated through Monte Carlo simulations, demonstrate that the RJD scheme offers a practical trade-off between coverage and secrecy performance. In particular, the protection region radius can be optimized to enhance overall system efficiency. The proposed framework provides valuable insights into the design of low-complexity and hardware-efficient jamming coordination scheme for securing HAPS-based communication networks.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".