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Record W2154887416 · doi:10.1109/icc.2011.5963273

Stackelberg Game on the Boundary of Coexistence

2011· article· en· W2154887416 on OpenAlexaff
Kandasamy Illanko, Alagan Anpalagan, D. Androutsos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStackelberg competitionMathematical optimizationComputer scienceBoundary (topology)Constraint (computer-aided design)Power (physics)Game theoryFeasible regionConvex optimizationSet (abstract data type)Regular polygonMathematical economicsMathematics

Abstract

fetched live from OpenAlex

This paper combines Convex analysis and Game theory to investigate the problem of maximizing the sum rate of transceivers operating in the same frequency band. In our earlier work, we proved that for transceivers operating under a total power constraint, the power distribution that maximizes the sum rate lies on the boundary of the feasible set formed by the power constraint. In this paper, we first prove that for two users, the sum rate is convex on the boundary formed by the line segment representing the power constraint, and the maximum sum rate is achieved when all the power is allocated to one of the users. Obviously, such a power allocation is unfair to the other user. We consider a scenario in which the first user is willing to sell some of the power allocated to him to the second user. We use Stackelberg Game theory to analyze this scenario and prove the existence of a unique competitive equilibrium. We derive the best response functions, and determine the optimum price the first user must charge and the optimum amount of power the second user should buy at this price. We also use simulation to obtain the best response functions and the equilibrium point, and demonstrate their agreement with our analytical results.

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: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.257

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 head0.031
GPT teacher head0.215
Teacher spread0.184 · 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
GenreEmpirical

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
Published2011
Admission routes1
Has abstractyes

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