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Record W2105893007 · doi:10.1109/vetecf.2002.1040314

Propagation-measurement-based predictions of RAKE receiver performance in W-CDMA systems operating in urban microcells

2003· article· en· W2105893007 on OpenAlexaff
Nikhil Adnani, R.J.C. Bultitude, Roshdy H. M. Hafez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsCarleton UniversityCommunications Research Centre Canada
Fundersnot available
KeywordsRake receiverMultipath propagationComputer scienceCode division multiple accessRakeTelecommunications linkElectronic engineeringDelay spreadAutocorrelationWidebandInterference (communication)Multipath interferenceSpread spectrumBit error rateSignal-to-interference-plus-noise ratioChannel (broadcasting)Computer networkEngineeringPhysicsMathematicsStatisticsPower (physics)

Abstract

fetched live from OpenAlex

A service specification of third generation wireless systems using wideband CDMA is to enable the delivery of data rates up to 2 Mbps to a single user on the downlink channel. One implication of such high data rates is the reduction of spreading gain, which results in increased multiple-access interference and also self-interference (SI). The latter is the result of multipath propagation and the imperfect autocorrelation properties of the spreading sequences that must be used for despreading. SI is estimated using a semi-analytical approach when the number of distinct multipath groups, each the vector sum of all multipath signals received within the delay resolution of the receiver, and their strengths are estimated using measured data. This in turn is used to predict bit error rates as a function of signal-to-noise ratio when a RAKE receiver is used in a mobile terminal.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.585
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.249
Teacher spread0.213 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

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