Physical layer secrecy for wireless communication systems using adaptive HARQ with error contamination
Bibliographic record
Abstract
This paper proposes a physical layer coding scheme to provide reliability and security in wireless communication systems. An Adaptive Granular Hybrid Automatic Repeat re- Quest (AG-HARQ) scheme based on Low-Density-Parity-Check (LDPC) codes is proposed. In the AG-HARQ protocol, the whole codewords are first transmitted (by Alice) to the intended receiver (Bob) and potentially intercepted by an unauthorized receiver or eavesdropper (Eve). Whenever the legitimate receiver (Bob) fails to decode the correct message, the received codewords are splitted into sub-packets and Bob computes a decoding confidence index for each of these sub-packets. Bob can then request a retransmission of those sub-packets having the lowest confidence indexes. These sub-packets are requested by Bob until correct decoding or until a maximum number of retransmissions is reached. An error contamination (EC) mechanism is also proposed which spreads the errors in the current frame with a scrambler and to the other frames by use of block interleavers. The proposed scheme ensures reliable and secure communication even in the case of negative Security Gap (SG) where the eavesdropper's channel benefits from better channel conditions than the legitimate channel.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".