Misbehavior detection in amplify-and-forward cooperative OFDM systems
Bibliographic record
Abstract
The success of cooperative communications hinges on the reliability of cooperating nodes between the communicating parties, which may not be guaranteed in realistic scenarios. To protect cooperative networks from selfish misbehaviors and malicious forwarding, a security mechanism monitoring various abnormal behaviors during cooperation is necessary because of the constantly changing network topology and variability of cooperating nodes. In this paper, based on the orthogonal time division protocol commonly used in cooperation, a misbehavior detection scheme is proposed for amplify-and-forward (AF) cooperative orthogonal frequency division multiplexing (OFDM) systems by introducing the time division duplexing (TDD) feature into the source node and exploiting the correlation properties between the transmitted and received signals. With the estimated amplification gain and noise power, two binary hypothesis tests are employed to detect power-reducing selfish behaviors and malicious jamming attacks respectively. Simulation results demonstrate the effectiveness of the proposed scheme in detecting different misbehaviors of cooperating nodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".