Improving wireless secrecy rate via full-duplex relay-assisted protocols
Bibliographic record
Abstract
In this paper, we examine the use of a friendly full-duplex (FD) relay to increase the secrecy rate over a fading channel between the legitimate source and the destination in the presence of residual self-interference (SI) and eavesdropper. In particular, we consider two different protocols based on the FD capability of relay: 1) FD transmission (FDT), in which the FD-Relay receives and sends data concurrently; 2) FD-Relay with jamming (FDJ), where first, the FD-Relay simultaneously receives data and sends jamming to the eavesdropper; then, it forwards the data, while the source jams the eavesdropper. We first develop the secrecy rate expressions for half-duplex transmission (HDT), half-duplex with jamming (HDJ), FDT, and FDJ relaying protocols, and then use them to derive their performance properties in terms of the channel gains between nodes, eavesdropper types, and more importantly, the SI level in FD-Relay. We further investigate the non-convex power allocation problems for the developed FDT and FDJ to maximize the secrecy rate under the power constraints. In particular, we develop an efficient iterative algorithm based on the difference-of-two-concave-functions programming. Analytical and simulation results show the strong influence of SI level on the achieved secrecy rate of the FDT and the FDJ. For sufficiently low SI, FDT achieves a much higher secrecy rate than FDJ, HDJ, and HDT. However, for higher SI, FDJ becomes more effective in enhancing the achieved secrecy rate. The results also indicate that adaptive power allocation can significantly improve the performance and confirm that the proposed FDT and FDJ outperform the HDT and the HDJ.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".