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Record W4234708199 · doi:10.1002/wcm.631

Modeling and performance analysis of multi‐service wireless CDMA cellular networks using smart antennas

2008· article· en· W4234708199 on OpenAlexaff
Christian Hartmann, Nidal Nasser

Bibliographic record

VenueWireless Communications and Mobile Computing · 2008
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsComputer scienceCode division multiple accessSmart antennaCDMA spectral efficiencyComputer networkSpace-division multiple accessUMTS frequency bandsNear-far problemCode (set theory)Base stationAntenna (radio)TelecommunicationsElectronic engineeringDirectional antennaEngineering

Abstract

fetched live from OpenAlex

Abstract In this paper, we present an analytical model to assess the blocking capacity of multi‐service code division multiple access (CDMA) systems. We include smart antenna systems in our model and show how the capacity of CDMA systems can be improved if smart antennas are employed at the base stations. Applying smart antennas can actually transform CDMA systems from being interference limited to being channel/code limited. To investigate this effect, we extend our model to include the limitation of channelization codes in CDMA‐based universal mobile telecommunication system (UMTS) systems. From the point of view of the call admission control (CAC) in a smart antenna CDMA system, we can either accept the capacity loss due to code limitation, or we can additionally apply space division multiple access (SDMA) techniques to re‐use channelization codes and thus re‐approach the capacity which is obtained if no code limitation is considered. Copyright © 2008 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.004
Research integrity0.0000.001
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 head0.066
GPT teacher head0.299
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.

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

Citations3
Published2008
Admission routes1
Has abstractyes

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