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Record W2765188317 · doi:10.1049/iet-com.2017.0338

Performance analysis of multi‐user scheduling in a spectrum sharing with OSTBC under correlated antennas in a cognitive radio system

2017· article· en· W2765188317 on OpenAlexaff
Mohammad Torabi, David Haccoun

Bibliographic record

VenueIET Communications · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCognitive radioComputer scienceScheduling (production processes)Rayleigh fadingCumulative distribution functionProbability density functionFadingAlgorithmChannel (broadcasting)Electronic engineeringTelecommunicationsMathematical optimizationWirelessMathematicsStatisticsEngineering

Abstract

fetched live from OpenAlex

This study presents a performance analysis for a cognitive radio network with multi‐user scheduling in which a primary user can share its licenced spectrum with several secondary users (SUs), each employing Alamouti orthogonal space‐time block coding (OSTBC) with spatially correlated antennas over Rayleigh fading channels. Closed‐form formulae are obtained for the cumulative density function of the signal‐to‐noise‐ratio of SUs with multi‐user scheduling under correlated antennas. Closed‐form mathematical expressions are then derived for three important system performance metrics: the average channel capacity, outage probability, and the average bit error rate of the system. From the numerical results obtained from the derived mathematical expressions, the system performances with different parameters are studied, evaluated and compared showing the effects of spatial correlation on the performance of the system. It is observed that spatially correlated antennas can improve the average channel capacity of the cognitive radio system with user scheduling.

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: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.553

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 communication0.0000.000
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 head0.032
GPT teacher head0.279
Teacher spread0.247 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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