Reliable and secure regions for the Gaussian wiretap channel using LDPC codes with granular HARQ
Bibliographic record
Abstract
This paper investigates reliable and secure transmissions over the Gaussian wiretap channel. A physical layer coding scheme based on Low-Density-Parity-Check (LDPC) codes with granular Hybrid Automatic Repeat reQuest(HARQ) protocol is presented. Increasing the HARQ granularity aims at sending coded data at the minimum rate required for legitimate successful decoding. This minimizes the information leakage that may benefit to eavesdropping. It will be shown that the granularity increases the frame error rate of the eavesdropper. Since the secrecy level can be assessed through the bit error rate (BER) at the unintended receiver, intraframe and interframe error contamination is employed to convert the loss of only few packets into much higher BERs. After obtaining the BERs at the legitimate and illegitimate receivers the reliable and secret regions can be determined. It is observed that with granular HARQ and interframe error contamination, regions that are reliable and secure at the same time are expanded.
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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 | 0.001 | 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".