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Record W1899474476 · doi:10.1109/icc.1995.524231

Effect of system imperfections on BER performance of CDMA correlator receiver

2002· article· en· W1899474476 on OpenAlexaff
J. Panicker, S. Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMultipath propagationFadingCode division multiple accessBit error rateElectronic engineeringRake receiverSpread spectrumComputer sciencePower controlDelay spreadTransmission (telecommunications)Phase (matter)Power (physics)AlgorithmTelecommunicationsEngineeringPhysicsDecoding methodsChannel (broadcasting)

Abstract

fetched live from OpenAlex

Direct sequence spread spectrum CDMA systems is receiving considerable attention in the field of mobile communication. The bit error rate performance of a code division multiple access correlator type receiver with imperfections in power control, carrier phase estimate and spreading code phase estimate is analyzed. Imperfection in power control is taken into account by modeling the received signal power as a log normally distributed random variable. The phase estimate errors are modeled as zero mean Gaussian random variables. It is shown that the performance of such a receiver significantly degrades when variance of power control imperfection is above 0 dB, standard deviation of the code phase error is above 0.01 and mean square error of carrier phase estimate is above 0.01. The communication system considered is a microcellular system operating in an indoor environment where K transmission stations are asynchronously transmitting over their individual multipath fading channels to the base station receiver.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2002
Admission routes1
Has abstractyes

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