Improving Secrecy under High Correlation via Discriminatory Channel Estimation
Bibliographic record
Abstract
In PHY-security, high correlation between main and wiretap channels, which are frequently observed, can cause a significant loss of secrecy. Unfortunately, signal processing techniques at the transmitter (Alice), such as precoding and artificial noise (AN) techniques, are ineffective. Under this circumstance, this paper focuses on a slowly fading and reciprocal channel scenario wherein Alice sends a confidential message to an authorised receiver (Bob) with the transmission overheard by a passive unauthorised receiver (Eve), and all of them are equipped with multiple antennas. To prevent interception and ensure secrecy, we redesign a novel scheme of discriminatory channel estimation (DCE), in which training procedures are developed to limit the channel estimation performance at Eve while producing little effect on Alice and Bob. As a result, Eve's ability to obtain the channel information would deteriorate, thereby effectively increase the difference in decoding the message between Bob and Eve. Simulation results demonstrate the proposed scheme could provide substantial gains with respect to secrecy.
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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 | 0.000 |
| 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".