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Record W2165058643 · doi:10.1109/glocom.1990.116794

Performance analysis of error control coding for mobile channels

2002· article· en· W2165058643 on OpenAlexaff
David Haccoun, F. Cagnon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFadingComputer scienceCoding (social sciences)Rician fadingDegradation (telecommunications)Probability of errorChannel (broadcasting)Diversity schemeAlgorithmUpper and lower boundsEnergy (signal processing)Channel codeTheoretical computer scienceTelecommunicationsMathematicsStatisticsDecoding methods

Abstract

fetched live from OpenAlex

New upper bounds on the bit error probability are applied to evaluate the error performance of coded systems over noninterleaved and partially interleaved Rician fading mobile channels. The correlation between successive received symbols is exploited to bound the error performance. The bound allows useful evaluation of coding gains on realistic communication systems without going into lengthy computer simulations. By further defining the maximum energy degradation factors, compact upper bounds are expressed in a way similar to the fully interleaved or memoryless channel. The maximum energy degradation factors are computed for a wide variety of mobile channel conditions. These factors give an interesting evaluation of the fading conditions and may be used to design coded communication systems on mobile channels. Independent space or frequency diversity may be taken into account in these bounds, and it is shown that the energy degradation is independent of this added independent diversity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 score0.855
Threshold uncertainty score0.264

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 science0.0000.000
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 head0.031
GPT teacher head0.271
Teacher spread0.241 · 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

Citations9
Published2002
Admission routes1
Has abstractyes

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