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Record W2003441926 · doi:10.1109/icumt.2012.6459785

A price setting approach to power trading in cognitive radio networks

2012· article· en· W2003441926 on OpenAlexafffund
Mahmoud Khasawneh, Anjali Agarwal, Nishith Goel, Marzia Zaman, Saed Alrabaee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsCistel Technology (Canada)Concordia University
FundersMitacs
KeywordsCognitive radioComputer scienceComputer networkProfit (economics)Quality of serviceRevenueTransmitter power outputRadio spectrumRentingKey (lock)Bandwidth (computing)TelecommunicationsChannel (broadcasting)WirelessComputer securityBusinessTransmitterEngineering

Abstract

fetched live from OpenAlex

Cognitive radio technology has been proposed to improve the spectrum utilization by sharing the frequency spectrum bands between the licensed and unlicensed users which are called primary users (PUs) and secondary users (SUs) respectively. The main objective of the SUs is to achieve their QoS by exploiting the unused spectrum while the PUs aim to get high profit by leasing their unused spectrum. Pricing and transmission power are two key issues of interest to PUs and SUs as well. In this paper, we propose a power pricing model wherein the PUs attain some revenue by renting their unused frequency to SUs that use suitable power levels to transmit which do not interfere with other users in the network. In our proposed model the SUs coexist with PUs in the same network where they can transmit over the same channel simultaneously. Performance evaluation of the proposed model demonstrates that the scheme helps in using the frequency spectrum more efficiently and increasing the gained profit of the PUs in comparison with the other existing models.

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 categoriesnone
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 score0.943
Threshold uncertainty score0.780

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 head0.019
GPT teacher head0.246
Teacher spread0.228 · 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
Published2012
Admission routes2
Has abstractyes

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