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Record W2119062975 · doi:10.1109/pimrc.2006.254422

Multiuser Detection in MIMO DS-CDMA Systems Over Slow-Fading Channels

2006· article· en· W2119062975 on OpenAlexaff
Mohamed AlJerjawi, Walaa Hamouda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsFadingCode division multiple accessMultiuser detectionComputer scienceMIMODetectorElectronic engineeringMaximal-ratio combiningInterference (communication)Signal-to-noise ratio (imaging)Diversity schemeAlgorithmTopology (electrical circuits)TelecommunicationsDecoding methodsEngineeringChannel (broadcasting)Electrical engineering

Abstract

fetched live from OpenAlex

Transmit diversity designed for fast-fading channels is examined in a multiuser direct-sequence code division multiple access (DS-CDMA) system over slowly-fading channels. The underlying space-time system employs two transmit antennas and M receive antennas at the user side and base-station receiver, respectively. The receiver employs a decorrelator multi-user detector. In our analysis, we derive a closed form expression for the bit error probability. These theoretical results, when compared to simulations, are shown to be very accurate. Both simulations and analytical results demonstrate that, regardless of the system load, the full diversity order of NM is always maintained and only a signal-to-noise ratio (SNR) loss is incurred when using a decorrelator detector at the receiver side. This SNR degradation is shown to be a function only of the number of users and independent of the number of transmit and/or receive antennas. Using our theoretical results, we show that the loss in SNR from the single-user bound can be well approximated by 10log(C/2) where C is a parameter of the interference level

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)0.001
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 score0.726
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.273
Teacher spread0.250 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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