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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".