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Record W2149896986 · doi:10.1109/sam.2004.1502951

Trace-orthonormal full rate full diversity linear triangular cyclotomic space-time codes minimizing worst case pair-wise error probability

2005· article· en· W2149896986 on OpenAlexaff
Jian‐Kang Zhang, Jing Liu, K.M. Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBlock codeMathematicsCoding gainOrthonormal basisAlgorithmLinear codeFadingDiversity gainQuadrature amplitude modulationFull RateUpper and lower boundsMIMOConcatenated error correction codePairwise error probabilityComputer scienceDiscrete mathematicsDecoding methodsTelecommunicationsBit error rateChannel (broadcasting)Physics

Abstract

fetched live from OpenAlex

We consider the flat fading wireless link where the number of transmitter antennas is greater than the number of receiver antennas and the channel is known to the receiver. For such systems, the currently available linear space-time block codes provide full rate and full diversity, but cannot guarantee that the optimal coding gain is achieved. In this paper, by taking advantage of delay we propose the trace-orthonormal full rate full diversity linear triangular cyclotomic space-time block codes, which actually is a generalization of the linear diagonal space-time block code. A universal lower bound on the worst case average pair-wise error probability is derived for any linear space-time block code. Particularly when the number of the transmitter antennas is equal to 2/sup m/, we prove that our proposed code minimizes the worst case average pair-wise error probability of the maximum likelihood detector for M-ary quadrature amplitude modulation (QAM). Therefore, in this case, it achieves the optimal coding gain.

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)0.006
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.244
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 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

Citations1
Published2005
Admission routes1
Has abstractyes

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