Truthful Mechanisms for Message Dissemination via Device-to-Device Communications
Bibliographic record
Abstract
Device-to-device (D2D) communications are not only featured by high spectral and energy efficiency, but also offer appealing benefits for applications, such as message dissemination, traffic offloading, and coverage expansion. In this paper, we consider a message dissemination scenario where a base station (BS) can direct message requests to be fulfilled by some source devices via D2D communications and thereby save the resource cost. Furthermore, a source device may be subject to a cost budget due to its limited resources. To minimize the overall cost or maximize the BS's cost saving from offloading, it is essential to properly assign the message request(s) for each source and decide the payment to incentivize participation. The request direction is an NP-hard problem, while the payment determination is also nontrivial. The payment should be sufficient to compensate for a source device's resource cost, and meanwhile, incentivize the source to truthfully declare its private cost and budget. Modeling the problem as a reverse auction, we develop both a deterministic mechanism and a randomized mechanism, which are truthful or truthful in expectation, respectively. Both mechanisms are individually rational and subject to a computational complexity lower than that of the well-known Vickrey-Clarke-Groves mechanism or its generalized version. While the deterministic mechanism performs well in an average sense, the randomized mechanism maintains an approximation guarantee for the worst case. Extensive numerical results demonstrate the effectiveness of the proposed mechanisms with various settings in the D2D content distribution scenario.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".