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Record W2163850699 · doi:10.1109/ccece.2004.1347648

Improved space-time trellis codes with three and four transmit antennas

2004· article· en· W2163850699 on OpenAlexaff
David Bernier, François Chan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTrellis (graph)Space–time trellis codeComputer scienceRayleigh fadingPhase-shift keyingTrellis modulationAlgorithmBlock codeTRACE (psycholinguistics)Code (set theory)MathematicsDecoding methodsTelecommunicationsFadingBit error rateConcatenated error correction code

Abstract

fetched live from OpenAlex

It has been shown that the rank and the determinant of the distance matrices are the design criteria for space-time trellis codes over quasi-static Rayleigh fading channels. Recently, a new design criterion that maximizes only the trace has been proposed for systems with a large product of the numbers of transmit and receive antennas. This criterion is useful and yields superior codes when the product of the numbers of receive and transmit antennas is at least 4. In this paper, new QPSK space-time trellis codes for 3 and 4 transmit antennas with up to 1024 states are presented. Our 64-state codes for 3 and 4 transmit antennas present a larger trace and provide a better error performance than previously published codes with the same number of states. If a smaller error probability is required, a further improvement can be achieved by increasing the number of states at the expense of additional complexity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2004
Admission routes1
Has abstractyes

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