Adaptive granular HARQ LDPC-based coding for secrecy enhancement in wiretap channels
Bibliographic record
Abstract
In this work, reliable and secure transmission over generic-Gaussian wiretap channel model is investigated. An Adaptive Granular Hybrid Automatic Repeat reQuest (AG-HARQ) protocol is proposed which tries to minimize the required rate for successful decoding by the legitimate parties while amplifying the privacy by minimizing the information leakage to a wiretapper. In the case of LDPC decoding failure at the legitimate receiver (Bob), a retransmission is requested until correct decoding or until the maximum number of transmitted packets is reached. As soon as Bob is able to correctly decode the LDPC codeword, the retransmissions are stopped to avoid any additional bits leakage to the eavesdropper (Eve). In our proposed method, to minimize the leakage, a confidence level index, Cj, for correct decoding is defined as the mean of absolute value of the Log-Likelihood Ratio (LLR). In the case of failure, only the sub-packets with the smallest Cjvalues will be retransmitted since they represent the most unreliable information sub-packets. Frame Error Rate (FER) is used as a metric to show the effectiveness of the proposed method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".