MétaCan
Menu
Back to cohort
Record W1896242582 · doi:10.1109/vetec.1999.778461

Multistage interference cancellation with multipath decorrelating for QPSK asynchronous DS/CDMA system over multipath fading

2003· article· en· W1896242582 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
KeywordsMultipath propagationRake receiverRakeComputer scienceFadingElectronic engineeringRayleigh fadingDelay spreadAsynchronous communicationSingle antenna interference cancellationCode division multiple accessInterference (communication)AlgorithmTelecommunicationsChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

A combined multistage interference cancellation (MIC) and multipath decorrelating scheme (MIC-DECO) for asynchronous QPSK DS/CDMA over frequency-selective multipath Rayleigh fading channels is introduced. Unlike the conventional MIC, which attempts to cancel the multiple-access interference (MAI) and total self interference (SI) the introduced scheme aims to remove the MAI, and partial SI incurred by the self intersymbol interference (SII). After cancellation, decorrelating is used first to separate the multipath signals and then to re-combine the resulting fading replicas for symbol decision. If the noise correlation in the fading replicas is known, an optimum combining structure (MIC-OPTM) can be achieved. Furthermore, when the MAI and SII are successfully removed, the MIC-OPTM can be replaced by the MIC using RAKE combining (MIC-RAKE) with reduced complexity. The simulation results show that the MIC-DECO, MIC-OPTM, and MIC-RAKE in a multi-user environment provide a good performance close to the ideal performance in a single-user system, and outperform 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 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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.968

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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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

Citations2
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207