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Record W2682915626 · doi:10.1109/ccece.2017.7946744

On the performance of OFDM Index Modulation over Nakagami fading channels

2017· article· en· W2682915626 on OpenAlexaff
Ibrahim Aboharba, Houssein Boud, Quazi Mehbubar Rahman, Raveendra K. Rao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingNakagami distributionFadingModulation (music)Computer scienceQuadrature amplitude modulationQAMElectronic engineeringPhase-shift keyingChannel (broadcasting)TelecommunicationsBit error rateEngineeringPhysics

Abstract

fetched live from OpenAlex

Recently, Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) has been introduced as a novel multi-carrier system to carry additional information bits compared to conventional OFDM. In this paper, we provide a closed-form expression of an OFDM-IM system for M-ary signal constellations such as M - QAM and M - PSK modulation schemes over a Nakagami-m fading channel. Simulation results illustrate that OFDM-IM improves system performance in high data-rate transmission over conventional OFDM under various Nakagami fading factor m.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.185

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 communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.248
Teacher spread0.227 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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