Enhancing secrecy of the Gaussian wiretap channel using rate compatible LDPC codes with error amplification
Bibliographic record
Abstract
This paper proposes a physical layer coding scheme to secure communications over the Gaussian wiretap channel. This scheme is based on non-systematic Rate-Compatible Low-Density-Parity-Check (RC-LDPC) codes. The rate compatibility involves the presence of a feedback channel that allows transmission at the minimum rate required for legitimate successful decoding. Whenever the decoding is unsuccessful, a feedback request is sent back by the intended receiver, favoring the legitimate recipient over an unauthorized receiver (eavesdropper). The proposed coding scheme uses a finer granularity rate compatible code to increase the eavesdropper decoding failure rate. However, finer granularity also implies longer decoding delays. For this reason, a rate estimator based on the wiretap channel capacity is used. For this purpose, a set of packets is sent at once and then successive small packets are added subsequently as needed until successful decoding by the legitimate receiver is achieved. Since the secrecy level can be assessed through the bit error rate (BER) at the unintended receiver, an error amplifier is proposed to convert the loss of only few packets in the wiretap channel into much higher BERs for the eavesdroppers. Simulation results show the secrecy improvements obtained in terms of error amplification with the proposed coding scheme. Negative security gaps can also be achieved at the physical layer.
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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".