Incentive Mechanism Design for Collaborative Physical Layer Authentication: A Centralized Governance Approach
Bibliographic record
Abstract
While physical layer authentication can mitigate wireless channel vulnerabilities, its reliability is often compromised by inherent noise and variability of observed physical layer attributes. As a solution, collaborative physical layer authentication (CPLA) introduces multiple nodes to enhance performance, but incurs additional computational and communication costs for collaborators. Without incentive, desired collaborators may act selfishly and withdraw, and involving unreliable collaborators could degrade performance. Therefore, this paper proposes an incentive mechanism with a new centralized governance approach to coordinate CPLA, engaging reliable collaborators to optimize authentication accuracy. Specifically, we model the interaction between the center and collaborators as a Stackelberg game to establish. To reduce redundant computations in equilibrium solving, we first construct a candidate pool containing potential trainable combinations. Subsequently, we design incentive and training schemes for each candidate combination. Moreover, a quality-driven combination selection scheme is proposed to maximize incentive effectiveness. Based on the candidate pool and strategies, it integrates a deep Q-network as collaborator quality manager and a combination-level evaluation module, and via “filter-then-verify” identifies optimal incentive targets with low complexity while improving authentication accuracy. Simulations demonstrate that the proposed scheme successfully incentivizes selfish collaborators and achieves 99% authentication accuracy in unreliable collaborative environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".