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Record W3022639242 · doi:10.1049/iet-com.2019.0666

Analysis and outage performance evaluation of a fair scheduling for independent non‐identically distributed users in a cognitive radio using OSTBC with equally correlated transmit antennas

2020· article· en· W3022639242 on OpenAlexaff
Mohammad Torabi, David Haccoun

Bibliographic record

VenueIET Communications · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCognitive radioComputer scienceIndependent and identically distributed random variablesScheduling (production processes)Outage probabilityComputer networkTelecommunicationsFadingRandom variableMathematical optimizationMathematicsWirelessDecoding methodsStatistics

Abstract

fetched live from OpenAlex

In this study, a fair user‐scheduling method is considered for an underlay cognitive radio system, in which the secondary users (SUs) are sharing the licenced frequency spectrum of a single primary user (PU), where the PU and SUs utilise Alamouti orthogonal space‐time block coding (OSTBC). The impacts of some important practical issues are investigated in the system. First, it is assumed that the signal‐to‐noise‐ratios of the SUs are independent non‐identically distributed. Second, the transmit antennas for OSTBC corresponding to the SUs are assumed to be equally correlated. Third, the interference from the PU to SUs as well as the interferences from SUs to the PU are taken into consideration. To investigate the outage performance of the SUs, a closed‐form expression for the cumulative distribution function of the SUs signal‐to‐interference‐noise‐ratio is obtained and then used to derive an expression for evaluating the secondary system outage performance. The numerically evaluated results, validated by computer simulations, provide insights about system outage performance under various practical situations on the impacts of the spatial antennas correlation and the PU interference on to SUs.

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: none
Teacher disagreement score0.517
Threshold uncertainty score0.574

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 science0.0000.000
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 head0.050
GPT teacher head0.298
Teacher spread0.249 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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