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Record W2536457415 · doi:10.1587/comex.2016xbl0159

Performance comparison of IM-based OFDM and OFDM with multiple constellations

2016· article· en· W2536457415 on OpenAlexaff
Binh Vo, Ha H. Nguyen

Bibliographic record

VenueIEICE Communications Express · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingPrecodingConstellationComputer scienceMultipath propagationElectronic engineeringRayleigh fadingFadingModulation (music)Spectral efficiencyTelecommunicationsChannel (broadcasting)MIMOEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper compares performance of index modulation (IM) based OFDM to a more flexible version of OFDM, known as OFDM with multiple constellations (OFDM-MConst). To deliver the same spectral efficiency as in the IM-based OFDM, the OFDM-MConst employs different constellations in different subcarriers. The performance comparison between IM-based OFDM and OFDM-MConst is done for both cases of “no precoding” and “with precoding” of data symbols. Thanks to the multipath diversity gain achieved with precoding and enjoyed by all the information bits, the precoded-OFDM-MConst is shown to significantly outperform precoded-IM-based-OFDM systems over frequency-selective Rayleigh fading channels.

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 categoriesnone
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 score0.495
Threshold uncertainty score0.469

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.264
Teacher spread0.237 · 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.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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