An Incentive Mechanism Design View for Hybrid Access in Small Cell Networks: Keeping a Secret Is Not Smart
Bibliographic record
Abstract
In this paper, we investigate the hybrid access control policy in two-tier small cell networks from the perspective of incentive mechanism design, considering macro-cell base station's (MBS) private information. Then, we formulate this problem as a Stackelberg game. To be specific, the MBS and small cell base stations (SBSs) are modeled as leader and followers, respectively. A subsidy mechanism is adopted by MBS when the SBS can provide acceptable service level for macro user equipment. Moreover, we consider the impacts of MBS's private information on the Stackelberg equilibrium (SE) of the proposed game, and we present the equilibrium analysis and relationship under different available information circumstances. To obtain relatively satisfactory outcome for both MBS and SBS, we discuss the design of bargaining scheme based on the SE. Theoretical analysis and simulation results show that it is better for MBS to broadcast the private information to get more payoff from the perspective of incentive mechanism design.
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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 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".