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Record W1181605978 · doi:10.1049/iet-com.2014.0720

Performance analysis of orthogonal space–time block coding with antenna selection

2015· article· en· W1181605978 on OpenAlexaff
Mohammad Torabi, Jean Conan

Bibliographic record

VenueIET Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTransmitterCoding gainBlock codeBit error rateQuadrature amplitude modulationAntenna (radio)Computer scienceAlgorithmCoding (social sciences)MathematicsQuadrature (astronomy)Phase-shift keyingQAMSelection (genetic algorithm)TelecommunicationsTopology (electrical circuits)Electronic engineeringStatisticsDecoding methodsChannel (broadcasting)CombinatoricsArtificial intelligence

Abstract

fetched live from OpenAlex

A new approach for performance analysis of orthogonal space–time block coding systems with antenna selection is presented. Antenna selection has been performed at the transmitter and/or at the receiver sides. Closed‐form expressions are derived for the approximate average bit error rate (BER) of the considered system for M ‐ary quadrature amplitude modulation and phase‐shift keying schemes. Different from other approaches presented in the literature, analytical expressions are derived for the average signal‐to‐noise ratio (SNR) gain obtained from each antenna selection scheme, and then using those SNR gains closed‐form expressions are obtained for the approximate average BER performance. The system performances of several forms of the presented scheme are evaluated and compared. It is shown that the results obtained from the mathematical expressions match closely with simulation results.

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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.025
GPT teacher head0.259
Teacher spread0.234 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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