Physical layer secrecy performance of multiple antennas transmission with partial legitimate user CSI
Bibliographic record
Abstract
Conventional beamforming transmission techniques can enhance physical layer secrecy performance while requiring the full channel state information (CSI) of legitimate users and even that of eavesdroppers at the transmitter. However, providing full CSI of legitimate users at the transmitter can be challenging in practice. Thus, it is of considerable interest to enhance secrecy performance with partial CSI of legitimate users at the transmitter. Random unitary beamforming (RUB) is a low‐complexity multiple antennas transmission scheme requiring limited CSI. In this study, the authors investigate the secrecy performance of RUB transmission over multiple‐input single‐output single‐eavesdropper and multiuser multiple‐input multiple‐output single‐eavesdropper channels. They also propose a novel RUB‐based artificial noise (AN) method for multiple antennas communication system. They derive the closed‐form expressions of the exact and the asymptotic ergodic secrecy rate and the secrecy outage probability for these transmission scenarios. Numerical results are presented to illustrate the trade‐off between performance and complexity of the resulting physical layer security design. They show that the deployment of RUB and RUB‐based AN offers an attractive solution for enhancing the security of wireless transmission systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".