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Record W2110614158 · doi:10.1109/glocom.2007.920

Equilibrium and Disequilibrium Pricing for Spectrum Trading in Cognitive Radio: A Control-Theoretic Approach

2007· article· en· W2110614158 on OpenAlexaff
Dusit Niyato, Ekram Hossain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDisequilibriumCognitive radioComputer scienceFrequency allocationNash equilibriumGame theoryRevenueMicroeconomicsEconomicsComputer networkTelecommunicationsFinance

Abstract

fetched live from OpenAlex

Spectrum trading is a concept used to describe the economics of dynamic spectrum sharing in cognitive radio networks. In this paper, we consider the problem of spectrum trading between primary and secondary services. Multiple markets are established in this trading for the multiple available frequency bands. The primary service is considered as the seller and the secondary service is considered as a buyer in a dynamic spectrum trading market. The spectrum supply from the primary service is derived based on the revenue earned due to spectrum sharing and the cost due to QoS performance degradation of the connections served by the primary service. The spectrum demand of secondary service is obtained based on the utility from spectrum usage. We first obtain the equilibrium pricing which is the point where spectrum supply equals spectrum demand. Then, we consider the case where the spectrum sellers do not offer the equilibrium price. The impact of this disequilibrium pricing is investigated. We model the cases of equilibrium and disequilibrium pricing as feedback control systems in which the spectrum seller and buyer have their own transfer functions. By using classical control theory the stability of the model is analyzed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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.

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

Citations21
Published2007
Admission routes1
Has abstractyes

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