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Record W2134200094 · doi:10.1109/icc.1999.767893

Multistage interference cancellation with diversity reception for QPSK asynchronous DS/CDMA system over multipath fading channels

2003· article· en· W2134200094 on OpenAlexaff
Jianfeng Weng, Guoqiang Xue, Tho Le‐Ngoc, Sofiène Tahar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsRakeRake receiverComputer scienceMultipath propagationCode division multiple accessSingle antenna interference cancellationFadingRayleigh fadingMaximal-ratio combiningAlgorithmElectronic engineeringSpread spectrumDiversity schemeMultiuser detectionPhase-shift keyingBit error rateChannel (broadcasting)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A multistage interference cancellation (MIC) technique with RAKE diversity (MIC-RAKE) for QPSK asynchronous direct sequence code division multiple access (DS/CDMA) system over frequency selective multipath Rayleigh fading channels is presented. Unlike the conventional MIC, which tries to subtract the lump sum of the multiple access interference (MAI) and the self-interference (SI), the MIC-RAKE attempts to cancel the MAI and the partial SI, and to treat the residual SI as useful signal for symbol decision. The RAKE combining is employed to collect signal replicas over multiple fading paths. The upper and lower bounds on the bit error probability are derived by using a Gaussian approximation. Furthermore, the effect of the channel estimation error is studied. Analysis and simulation show that the MIC-RAKE can provide a performance close to the ideal performance of single-user system, and outperforms the conventional MIC even in the presence of channel estimation error.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.274
Teacher spread0.235 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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