Covert Communication in Two-Hop Cooperative Cognitive Radio System
Bibliographic record
Abstract
Covert communication is capable of enhancing user privacy by protecting communication behavior. In this article, by employing overlay and underlay spectrum access modes, we investigate a covert cooperative cognitive radio (CCCR) system, where primary transmitter (PT) and secondary transmitter (ST) cooperate with each other to secretly transmit their own confidential information. Specifically, PT can initiate the transmission directly or transmit information with the aid of ST. In return, ST is able to transmit information by exploiting PT's spectrum. In our system, we assume that both PT and ST send artificial noise (AN) to confuse eavesdropper (Eve). That is to say, ST sends AN to Eve when PT initiates the transmission and PT sends AN to Eve when ST transmits information. Then, we explore Eve's detection scheme and obtain the closed-form expression of the minimum detection error probability at Eve in the two modes. We further analyze covert rate and covert outage probability (COP) of primary receiver (PR) and secondary receiver (SR) in different modes. Numerical results show that the interference power dominates the covert transmission, while the self-interference has little impact on the covert performance. Furthermore, it is observed that PR can obtain better covert performance in underlay mode, while SR can obtain better covert performance in overlay mode.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".