Secrecy capacity of artificial noise aided Rician/Rayleigh MIMO channels
Bibliographic record
Abstract
Recent research on physical security of wireless systems focus on artificial noise aided security. The main metric for analysis of such systems is the secrecy capacity of the system. Most of the AN schemes proposed in recent research are based on an hypothesis that the number of transmit antennas is larger than that of the receiver antennas. Under this assumption the system can utilize all eigen-subchannels, equal to number of receiver antennas, of a MIMO system to send messages, and use remaining null spaces for transmitting AN signals. These AN signals null out at the legitimate receivers and degrade illegitimate receiver's channels. However, this strategy can significantly impair the secrecy capacity of the system if number of transmit antennas is constrained or even smaller than number of receive antennas. Recently, a new strategy has been proposed, where messages are encoded in s (which is a variable) strongest eigen-subchannels based on ordered eigenvalues of Wishart matrices, while AN signals are generated in remaining spaces. In this paper, this strategy has been extended to Rician channels. The transmitter-receiver link is supposed to be effected by Rician fading while the illegitimate link is experiencing Rayleigh fading and consequently the secrecy capacity of such system is computed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".