Secrecy Rate Analysis of mmWave MISO Ad Hoc Networks with Null Space Precoding
Bibliographic record
Abstract
Secure communication in the millimeter wave (mmWave) network is an important issue in the next-generation wireless network due to the massive improvement in the eavesdroppers' ability. This paper studies the secrecy rate performance of a mmWave multi-input single-output (MISO) ad hoc network in the presence of colluding eavesdroppers. Firstly, to enhance the average achievable secrecy rate, an artificial noise (AN) transmission with null space linear precoder (Tx-ANLP) is applied, taking into consideration the effect of blockage and Nakagami fading. Consequently, the tools of stochastic geometry are used to derive the mathematical expression of the average achievable secrecy rate for mmWave MISO ad hoc network with Tx-ANLP technique. Numerical and simulation results show that, using the Tx-ANLP technique achieves more than three-fold improvement in the average secrecy rate over that without in the high power transmit regime (>15dBm). Moreover, the effect of increasing the colluding eavesdroppers' intensity without using the Tx-ANLP technique is studied which provides a high deterioration in the average secrecy rate. Conversely, when the Tx-ANLP technique is applied, increasing the colluding eavesdroppers' intensity has no negative impact on the secrecy rate. Furthermore, the proper power allocation between the message and AN signals which maximizes the average secrecy rate is computed. The results therefore show that the Tx-ANLP 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 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".