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Record W2108747204 · doi:10.1109/vetec.1996.501397

Robustness of the reference symbol assisted multistage successive interference cancelling receiver with imperfect parameter estimates

2002· article· en· W2108747204 on OpenAlexafffund
A.C.K. Soong, Witold A. Krzymień

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobustness (evolution)TransmitterControl theory (sociology)Computer scienceAmplifierRake receiverSynchronization (alternating current)Electronic engineeringCode division multiple accessInterference (communication)Channel (broadcasting)FadingTelecommunicationsBandwidth (computing)Engineering

Abstract

fetched live from OpenAlex

This paper investigates the sensitivity to system imperfections of a multistage successive interference cancelling receiver with reference symbol assisted channel estimation. In particular, performance losses due to nonideal transmitter power amplifier gating, imperfect power control and synchronization errors in the RAKE receiver are determined. The results show that for path loss exponent of 4. Imperfect amplifier gating causes a relatively minor decrease in the system capacity while no such effect is observed for path loss exponents of 2 and 3. As expected, relaxing of power control has a similar capacity reducing effect. In spite of these two effects the resultant capacity is still significantly higher than that with the conventional matched filter receiver. Chip synchronization errors of the order to be expected in a properly designed CDMA system also have only minimal influence on performance. Therefore, conventional synchronization algorithms should perform adequately with successive interference cancelling receivers considered in the paper.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.056
GPT teacher head0.279
Teacher spread0.222 · 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

Citations6
Published2002
Admission routes2
Has abstractyes

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