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Record W2474567723 · doi:10.1109/ict.2016.7500459

Simple semi-analytical expression for the max-SIR outage probability in cellular networks

2016· article· en· W2474567723 on OpenAlexaff
Maher Arar, Elham Kalantari, Abbas Yongaçoğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoverage probabilityExpression (computer science)ExponentInterference (communication)Outage probabilityMathematicsProbability distributionSimple (philosophy)Topology (electrical circuits)Path lossHexagonal crystal systemConditional probabilityGridComputer scienceAlgorithmStatisticsCombinatoricsDecoding methodsTelecommunicationsGeometryFadingWirelessChannel (broadcasting)

Abstract

fetched live from OpenAlex

Using the hexagonal grid based two-interferer model we derive a simple analytical expression for the max-SIR conditional outage probability in an interference-limited cellular network. The derived probability is compared to the one that is obtained by simulation from the 19 base stations two-ring hexagonal grid. The comparison reveals that the derived probability matches the actual one to within 0-2.5 dB for practical values of the path loss exponent and the shadowing standard deviation. While no closed form expression is derived for the cell's outage probability (as the integral has no closed-form solution) its value is obtained through numerical integration. The cell's outage probability is shown to match, to within a fraction of a dB, the one provided by the two-ring hexagonal model. The derived expression can be used to obtain the cell's outage probability (or its complement, i.e. the cell's coverage probability) for a shadowing standard deviation of between 6 to 12 dB and for a path loss exponent of between 2.5 to 4.5.

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)0.002
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 score0.985
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0030.001
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 head0.053
GPT teacher head0.309
Teacher spread0.256 · 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
Published2016
Admission routes1
Has abstractyes

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