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Record W1546360764 · doi:10.1109/icupc.1995.496936

Performance of selection diversity for a DS CDMA system with synchronization errors

2002· article· en· W1546360764 on OpenAlexaff
M. Oğuz Sunay, P.J. McLane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCode division multiple accessRake receiverFadingComputer scienceSpread spectrumPhase-shift keyingDiversity schemeSynchronization (alternating current)Electronic engineeringRakeMultipath propagationAlgorithmIntersymbol interferenceSelection (genetic algorithm)Bit error rateTelecommunicationsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

Direct-sequence code division multiple access (DS CDMA), which was primarily used in military communications until the late 80s, has been the center of attention in cellular radio communications. An efficient series that is used to calculate the probability of error for a BPSK modulated DS CDMA system with chip timing and carrier phase errors in a slowly fading, multipath channel is derived. The receiver is assumed to be a coherent RAKE receiver that employs selection diversity. The error probability derivations require the characteristic functions of the intersymbol interference and the multiple access interference and these functions have been expressed in terms of the confluent hypergeometric function. The series is then used to assess the reduction in the system capacity due to different levels of synchronization errors. Systems of 1.25 MHz, 5 MHz and 10 MHz bandwidths are considered for different number of diversity branches.

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)0.007
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.235
Teacher spread0.205 · 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

Citations9
Published2002
Admission routes1
Has abstractyes

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