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Record W2894656185 · doi:10.1109/jsyst.2018.2871115

An Incentive Mechanism Design View for Hybrid Access in Small Cell Networks: Keeping a Secret Is Not Smart

2018· article· en· W2894656185 on OpenAlex
Youming Sun, Zhiyong Du, Qihui Wu, Yuhua Xu, Alagan Anpalagan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Systems Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsToronto Metropolitan University
FundersNational Natural Science Foundation of China
KeywordsStackelberg competitionPrivate information retrievalMechanism designComputer scienceIncentiveGame theoryStochastic gameBase stationComputer networkMacroMathematical optimizationComputer securityMicroeconomicsEconomicsMathematics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.638
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.283
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it