Robust Resource Allocation to Enhance Physical Layer Security in Systems With Full-Duplex Receivers: Active Adversary
Bibliographic record
Abstract
We propose a robust resource allocation framework to improve the physical layer security in the presence of an active eavesdropper. In the considered system, we assume that both legitimate receiver and eavesdropper are full-duplex (FD) while most works in the literature concentrate on passive eavesdroppers and half-duplex (HD) legitimate receivers. In this paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the secrecy data rate of the legitimate transmission. In the literature, assuming that the receiver operates in HD mode, secrecy data rate maximization problems subject to the power transmission constraint have been considered in which cooperating nodes act as jammers to confound the eavesdropper. This paper investigates an alternative solution in which we take advantage of FD capability of the receiver to send jamming signals against the eavesdroppers. The proposed self-protection scheme eliminates the need for external helpers. Moreover, we consider the channel state information uncertainty on the links between the active eavesdropper and other legitimate nodes of the network. Optimal power allocation is then obtained based on the worst-case secrecy data rate maximization, under a legitimate transmitter power constraint in the presence of the active eavesdropper. Numerical results confirm the advantage of the proposed secrecy design and in certain conditions, demonstrate substantial performance gain over the conventional approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".