Joint Optimization of 3-D Placement and Transmission Power for a Relay Based Covert Communication System
Bibliographic record
Abstract
Covert communication is a significant scenario towards 6G, where the transmission between transmitter and receiver should be covert without being detected. In covert communication, the transmitter called Alice transmits signal to the receiver called Bob, and a detecter called Willie continuously observes its received signal. Alice should control the transmit power under a certain value to mislead Willie judging the received signal containing only White Gaussian Noise. Several works have been carried out focusing on the covert communication performance from the viewpoint of timeliness, throughput, etc. However, the covert relay communication, especially the UAV based relay in transparent forwarding manner (TFM) is scarcely considered. In this paper, we study a novel scenario for strict and deteriorative covert communication with the UAV based relay: 1) The transmission of both Alice and the UAV relay cannot be detected by Willie. 2) The UAV must be on the sight of Willie. The three-dimensional placement of the UAV, the transmit power of Alice, and the amplifier gain of the UAV with TFM are jointly optimized by geometric programming, where several Lemmas are also derived. Finally, the performance of the proposed algorithm is verified by extensive simulations.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".