Reliable and secure communications over Gaussian wiretap channel using HARQ LDPC codes and error contamination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. HARQ granularity aims at sending coded data at the minimum rate required for legitimate successful decoding while minimizing the information leakage that may benefit to eavesdropping. It will be shown that the granularity increases the frame error rate at the eavesdropping receiver. Since the secrecy level can be assessed through the bit error rate (BER) at the unintended receiver, intraframe and interframe error contaminations are employed to convert the loss of only few packets in the wiretap channel into much higher BERs at the eavesdropper. From the BERs at the legitimate and illegitimate receivers, the reliability and security regions can be determined. It is observed that with granular HARQ and interframe error contamination, signal to noise (SNR) regions that are simultaneously reliable and secure are expanded significantly.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it