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Record W2111288443 · doi:10.1109/acssc.2005.1599737

Game Theoretic Optimal Transmission Strategies in Multipacket Reception Sensor Networks

2006· article· en· W2111288443 on OpenAlexaff
Minh Hanh Ngo, Vikram Krishnamurthy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWireless sensor networkNash equilibriumComputer scienceTransmission (telecommunications)Channel (broadcasting)Game theoryState spaceChannel state informationMathematical optimizationState (computer science)Potential gameWirelessComputer networkDistributed computingAlgorithmMathematicsTelecommunicationsMathematical economics

Abstract

fetched live from OpenAlex

Decentralized channel state information is exploited for optimal transmission policies for non-cooperative sensors in wireless networks with the multipacket reception capability. The problem is formulated as a non-cooperative game of a finite number of players and actions with a continuous channel state space. We prove the existence of a Nash equilibrium at which every sensor adopts a threshold policy. A stochastic gradient based algorithm that can be used to adaptively estimate the optimal transmission threshold for a sensor is proposed. The theoretical results and the tracking capability of the algorithm are illustrated via numerical examples

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 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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.244
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations3
Published2006
Admission routes1
Has abstractyes

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