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Record W2130742125 · doi:10.1109/vetecf.2009.5379057

A Distributed Framework with a Novel Pricing Model for Enabling Dynamic Spectrum Access for Secondary Users

2009· article· en· W2130742125 on OpenAlexaff
Soumitra Dixit, Shalini Periyalwar, Halim Yanıkömeroğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer networkComputer scienceBase stationHandoverWirelessCognitive radioLeaseIncentiveService providerService (business)TelecommunicationsBusiness

Abstract

fetched live from OpenAlex

Wireless Service Providers (WSPs) aim to maximize the revenue from their investment in spectrum lease and infrastructure. In this paper, we present a simple model for enabling temporary wireless access for Secondary (unsubscribed) Users (SUs) along with Primary (subscribed) Users (PUs) to the same Base Station (BS), allowing SU access provided there is unutilized spectrum available at the BS after all the PUs have been served. We develop a distributed framework focusing on the efficient utilization of the spectrum leased by a WSP, in contrast to the centralized approach based on spectrum/spectrum information pooling prominent in literature. This paper includes a detailed signaling framework along with a novel Differentiated Service Code Point (DSCP) based mechanism for distinguishing PUs from SUs at the BS. A novel incentive based pricing model for SUs with an inherent property of resource management at the BS is proposed along with a criterion for autonomous network selection at the SU terminal and a SU terminal initiated price based handoff scheme. The distributed approach proposed in this paper, provides a framework for a single WSP to maximize its profits by allowing SU access, without the need for coordination with other WSPs through a centralized mediating entity.

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 score0.695
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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