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Record W1971547627 · doi:10.1049/iet-com.2011.0765

Layered space-time multiple-input multiple-output detector with parameterisable performance

2012· article· en· W1971547627 on OpenAlexaff
Amirhossein Alimohammad, Santosh Nagaraj, Saeed Fouladi Fard, B.F. Cockburn

Bibliographic record

VenueIET Communications · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of AlbertaTransAlta (Canada)
Fundersnot available
KeywordsDetectorMIMOComputer scienceComputational complexity theoryAlgorithmWirelessChannel (broadcasting)Control theory (sociology)Mathematical optimizationMathematicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

This brief presents an improved layered space-time symbol detection algorithm for multiple-input multiple-output (MIMO) wireless systems. The proposed detection scheme utilises a different layer-ordering strategy than the so-called optimal ordering used in the conventional Bell Laboratories Layered Space-Time (BLAST) algorithm. To calculate the nulling vectors and their associated ordering, instead of using direct numerical techniques, the authors utilise a numerically stable iterative solution. It is shown that the performance of the proposed detector approaches closely that of an optimal maximum-likelihood detector at the expense of greater computational complexity compared with BLAST. Utilising the proposed parameterisable detector, one can trade off between the computational requirement and the error-rate performance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0020.000
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 head0.025
GPT teacher head0.242
Teacher spread0.217 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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