MétaCan
Menu
Back to cohort
Record W1534002221 · doi:10.1109/pimrc.2005.1651474

Comparison of Multiuser Detection Techniques for Asynchronous Multirate DS-CDMA Systems

2006· article· en· W1534002221 on OpenAlexaff
Bin Yang, Florence Danilo-Lemoine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsMultiuser detectionSingle antenna interference cancellationComputer scienceCode division multiple accessAsynchronous communicationRayleigh fadingMultipath propagationDetectorChannel (broadcasting)DecorrelationBit error rateAlgorithmInterference (communication)Spread spectrumElectronic engineeringFadingReal-time computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper compares performance of various multiuser receiver structures for asynchronous multirate DS-CDMA systems over multipath Rayleigh fading channels. Bit-error-rates (BER) of the novel decorrelator based successive interference cancellation (DBSIC) detector and other commonly used suboptimum multiuser receivers such as the decor relating, minimum mean-square-error (MMSE), SIC, parallel interference cancellation (PIC) and decorrelating decision-feedback (DF) detectors are evaluated for variable processing gain (VPG) CDMA systems with both perfect and imperfect channel side information. Simulation results show that DBSIC outperforms all other considered multiuser detection techniques in various multirate scenarios including cases with more than two rates at the expense of some additional complexity. In particular, DBSIC provides gains in the case where some physical users have increased data rates (i.e. heavier system's load in terms of virtual users). It is also observed that, while all the receivers suffer degradation in performance in the case of imperfect channel estimates, DBSIC still outperforms the other detection schemes

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.350
Teacher spread0.309 · 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
GenreMethods

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

Citations4
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207