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Record W2032236908 · doi:10.1109/crowncom.2008.4562545

Resource Allocation for Cognitive Radios in Dynamic Spectrum Access Environment

2008· article· en· W2032236908 on OpenAlexaff
Dong In Kim, Long Bao Le, Ekram Hossain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of ManitobaUniversity of Waterloo
Fundersnot available
KeywordsCognitive radioComputer scienceThroughputQuality of serviceResource allocationConstraint (computer-aided design)Interference (communication)Computer networkFadingChannel (broadcasting)Power controlExploitWirelessTelecommunicationsPower (physics)Computer securityEngineering

Abstract

fetched live from OpenAlex

We investigate the dynamic spectrum sharing problem among primary and secondary users in a cognitive radio network subject to QoS constraints for secondary users and interference constraints for primary users. For a scenario where only mean channel gains from secondary users to primary receiving points, which are averaged over short-term fading, are available, we derive outage probabilities for secondary users and interference constraint violation probabilities for primary users. Based on the analysis, we develop a framework to perform joint admission control and rate/power allocation for secondary users such that statistical guarantees on the violation probabilities of both the QoS and the interference constraints are achieved. Spectrum access by the secondary users can exploit the time-varying nature of the activity of the primary users, and thereby much higher throughput can be achieved compared to the case where primary users are assumed to be active at all time. Also, via extensive numerical analysis, throughput performances of primary and secondary users are investigated considering different levels of implementation complexity due to channel estimation.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.554

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 head0.020
GPT teacher head0.253
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.

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

Citations12
Published2008
Admission routes1
Has abstractyes

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