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Record W2148795300 · doi:10.1109/tcomm.2009.12.0701392

The BER optimal linear rake receiver for signal detection in symmetric alpha-stable noise

2009· article· en· W2148795300 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRake receiverBit error rateNoise (video)RakeSignal-to-noise ratio (imaging)Detection theoryMathematicsSIGNAL (programming language)AlgorithmComputer scienceElectronic engineeringControl theory (sociology)FadingTelecommunicationsStatisticsEngineeringDecoding methods

Abstract

fetched live from OpenAlex

The optimal linear Rake receiver for the detection of signals contaminated by symmetric alpha stable noise is derived for 1 < ¿ ¿ 2. Although this structure is suboptimal, it is optimal among the class of linear Rake receivers in the sense of minimizing the bit error rate. The proposed receiver is a very simple form of diversity combiner for signal detection in symmetric alpha-stable noise. The bit error rate improvements of the optimal linear Rake receiver over the maximal ratio combiner and the equal gain combiner are also derived in the form of signal-to-noise ratio advantage.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.705

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.265
Teacher spread0.240 · 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