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Record W2171096760 · doi:10.1109/isspa.2007.4555526

Performance of decorrelator detector in space-time CDMA systems over quasi-static and frequency non-selective Nakagami-m fading channels

2007· article· en· W2171096760 on OpenAlexaff
Ariel Lionel Sacramento, Walaa Hamouda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsFadingCode division multiple accessNakagami distributionMultiuser detectionComputer scienceBit error rateDetectorElectronic engineeringInterference (communication)Fading distributionBase stationSpread spectrumDiversity schemeTelecommunicationsEngineeringDecoding methodsChannel (broadcasting)

Abstract

fetched live from OpenAlex

The performance of space-time spreading (STS) transmit diversity is examined in a multiuser direct-sequence code division multiple access (DS-CDMA) system over Nakagami-m slowly-fading channels. The space-time system employs N=2 transmit antennas and L receive antennas at the user side and base-station (BS) receiver, respectively. To combat the effect of multiuser interference, we employ the linear decorrelator multiuser detector. We derive theoretical expression for the bit-error-rate (BER), and assess the performance under different system loads and fading figures m. In particular, these results show that the diversity order of 2 mL is achieved and only a signal- to-noise ratio (SNR) loss is incurred.

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 score0.002
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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
Published2007
Admission routes1
Has abstractyes

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