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Record W4243853725 · doi:10.1109/tcomm.2007.906433

Precise Outage Analysis of Selection Diversity and Switched Diversity in Bandlimited Microcellular Systems With Cochannel Interference

2007· article· en· W4243853725 on OpenAlexaff
Kathiravetpillai Sivanesan, Norman C. Beaulieu

Bibliographic record

VenueIEEE Transactions on Communications · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFadingIndependent and identically distributed random variablesNakagami distributionRayleigh fadingDiversity combiningElectronic engineeringInterference (communication)Fading distributionComputer scienceMathematicsTopology (electrical circuits)TelecommunicationsControl theory (sociology)Random variableStatisticsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Precise outage probability performance analysis of a microcellular system with selection and switched diversities is considered. The microcellular system is assumed to follow a flat, slow Nakagami/Rayleigh fading model wherein the fading channels from the desired transmitter to all the receiver antennas are independent and identically distributed Nakagami channels, and all the fading channels for the interfering signals are independent, identically distributed Rayleigh channels. Three selection and switching criteria, namely, desired signal power, signal-to-interference power ratio, and total output power are considered. Unlike previous results, the system model under investigation takes into account the pulse shaping, the random delays, and the phase offsets of the interfering users. Two Nyquist pulse shapes, spectrum-raised-cosine and Beaulieu-Tan-Damen pulse shapes, are considered. Analytical outage probability expressions are derived for an arbitrary number of interferers, arbitrary diversity order, and arbitrary value of desired user fading parameter. The outage performances of the selection criteria are compared. The optimum switching thresholds for different switching criteria are formulated.

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 score0.638
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.250
Teacher spread0.215 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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