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An Investigation of Block Coding for Laplacian Noise

2012· article· en· W2109825189 on OpenAlexaff
Hua Shao, Norman C. Beaulieu

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2012
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRayleigh fadingComputer scienceGaussian noiseFadingBlock Error RateAlgorithmNoise (video)Bit error rateElectronic engineeringDecoding methodsTelecommunicationsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In many communication channels, the ambient disturbance is known through experimental evidence as well as theoretical considerations to deviate strongly from the Gaussian model. In order to evaluate the performance of digital signaling in such channels, the non-Gaussian noise must be properly modeled. The Laplace distribution is used here to model the non-Gaussian noise and study the performance of communication systems employing block data transmission in the presence of impulsive noise. Analytical expressions for assessing codeword error probability and bit error probability are derived for hard-decision decoders, soft-decision decoders and optimum maximum likelihood detectors in both nonfading and quasi-static Rayleigh fading scenarios. Numerical results are presented for different codes, block sizes and error rate levels. The characteristics of error performance are discussed for both nonfading and quasi-static Rayleigh fading channels.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.524
Threshold uncertainty score0.738

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 head0.037
GPT teacher head0.278
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 designBench or experimental
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

Citations18
Published2012
Admission routes1
Has abstractyes

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