Achievable Secrecy Rate in mmWave Multiple-Input Single-Output Ad Hoc Networks
Bibliographic record
Abstract
This paper analyzes the achievable secrecy rate in a millimeter wave (mmWave) multi-input single-output (MISO) ad hoc network in the presence of colluding eavesdroppers. First, using the tools of stochastic geometry, the mathematical analysis of the average achievable secrecy rate is performed, taking into consideration the impact of blockage and Nakagami-m fading. Moreover, a multi-array artificial noise transmission technique is applied at the typical transmitter (Tx-AN) to enhance the average secrecy rate. Consequently, the mathematical expression of the average achievable secrecy rate for mmWave MISO ad hoc network with Tx-AN technique is presented. Numerical and simulation results show that, at given values of colluding eavesdroppers' intensity and total transmit power, using Tx-AN technique achieves 56.4% improved average secrecy rate over that without. Furthermore, by applying the Tx-AN technique, increasing the colluding eavesdroppers' intensity provides no negative impact on the average secrecy rate. However, without the AN technique, the average secrecy rate faces a fast degradation when the intensity of colluding eavesdroppers increases. The results therefore show that the Tx-AN technique is a useful technique to enhance the secrecy performance of mmWave MISO ad hoc network in the presence of colluding eavesdroppers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".