A Sequential Posted Price Mechanism for D2D Content Sharing Communications
Bibliographic record
Abstract
In this paper, the incentive mechanism design issue for device-to-device (D2D) content-sharing communications is discussed. In literature, most of works are based on auction/game theory, where all content owners first report their ask prices/costs towards the base station (BS) which finally decides only one winner from them to transmit data towards the content requester. One disadvantage of these works is that content owners may be frequently activated to provide auction/game information (such as prices/costs), leading to high energy consumption, but finally may not win to gain benefit. To address this, we propose a sequential posted price mechanism where the BS sends offers with posted prices to content owners in sequence and activates only one owner each time. The BS stops sending new offers as long as there is already an owner accepting an offer or when the BS finds the expected cost of sending a new offer is larger than the cost of direct transmission. The optimal posted prices and offer- stopping rule of the BS are derived by the backward principle of dynamic programming. Simulation results show that the proposed mechanism can effectively limit the proportion of content owners being activated while the BS maintains an acceptable expected cost.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".