Optimal Power Allocation for Secure Multicarrier Relay Systems
Bibliographic record
Abstract
We study power allocation for secrecy rate maximization in a multicarrier decode-and-forward relay system, where an eavesdropper exists. We consider three transmission modes: in no communication, the source and the relay do not transmit at all; in direct communication, the source broadcasts signals during the first time slot and the relay does not forward any signal during the second time slot; in relay communication, the source broadcasts signals during the first time slot and the relay forwards the reencoded signals to the destination during the second time slot. Determining the transmission strategy adaptively on each subcarrier, the optimal source power and the optimal relay power over all subcarriers are derived to maximize the sum secrecy rate under a total system power constraint. In addition, a suboptimal power allocation scheme is proposed to substantially reduce the computational complexity. It is shown that the proposed suboptimal solution is asymptotically optimal in the limit as the number of subcarriers goes to infinity. Extensive numerical results are presented for various scenarios. In particular, the performance of the suboptimal scheme is very close to that of the optimal scheme even if the number of subcarriers is moderately small.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".