Cooperative jamming polar codes for multiple‐access wiretap channels
Bibliographic record
Abstract
The authors study cooperative security in the physical layer of wireless systems based on the recently developed polar codes for multiple access channel (MAC). Specifically, the authors first consider the case of a m ‐user MAC with external eavesdropper. Using polar alignment, the authors formulate a discrete optimisation problem where security and reliability criteria can be handled separately over the bases of a set of deterministic binary matrices. A discrete algorithm with incrementally polynomial complexity is used to maximise the uniform sum secrecy rate of users. Moreover, the proposed coding scheme is shown to achieve strong security for any subset of users. The authors next examine the case of minimum number of cooperative helpers to fulfil a feasible secrecy rate requirement at the legitimate user. Here, a low‐complexity suboptimal algorithm is presented with at most one helper more than the optimal solution. Using Tal–Sharov–Vardy implementation of MAC polar codes, secure polar coding is implemented for a 2‐user Gaussian wiretap MAC channel. The upper and lower bound of block error probability are compared, respectively, at the legitimate receiver and the eavesdropper. The results demonstrate clearly that with sufficiently long block length, strong secrecy with respect to the eavesdropper is achieved, while block error probability approaches 0.5.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 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 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".