Secure Terahertz Indoor Communications Using Blockage Feature-Based Artificial Noise in 6G
Bibliographic record
Abstract
Terahertz communication with abundant spectrum resources is envisioned as the key technology of 6G. Despite its narrow beam, terahertz transmission is still vulnerable to eaves-dropping attacks in indoor scenarios. In this paper, we propose a blockage feature-based artificial noise scheme to safeguard the indoor network in the presence of multiple access points (APs), users, and eavesdroppers. Those APs with blocked links to the typical user are selected to emit artificial noise to deteriorate the reception of eavesdroppers. Thus, communication security is ensured without escalating the instability of legitimate connections caused by the small coverage nature of terahertz beams. By comprehensively considering the propagation characteristics of terahertz, such as the three dimensions narrow beam and the human blocking effect, we derive the theoretical expressions of the connection outage probability, the secrecy outage probability, and the average number of perfect links per unit area. Numerical results demonstrate that the proposed scheme outperforms the traditional schemes in terms of connection stability and secrecy performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".